# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/xr/cxrgycnwn3a2engcpa6finswtqxdogftbffjavnh5ulttlgpbgyq.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xh/cxhcyt6y7iofxpx5fhkrcpiatyd6zclkxfs7hlxm2vw5njljdfsu.py
# Topologically Sorted Source Nodes: [batch_norm], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   batch_norm => var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
triton_red_fused__native_batch_norm_legit_1 = async_compile.triton('triton_red_fused__native_batch_norm_legit_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.reduction(
    size_hints=[32, 8192],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__native_batch_norm_legit_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 3, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused__native_batch_norm_legit_1(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 32
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 16
    x1 = (xindex // 16)
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + ((4096*x0) + (65536*(r2 // 4096)) + (131072*x1) + (r2 % 4096)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = triton_helpers.welford_reduce(
            tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0
        )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(
        tmp2_mean, tmp2_m2, tmp2_weight, 1
    )
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tl.store(out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr1 + (x3), tmp3, xmask)
    tl.store(out_ptr2 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bi/cbizslxpujifo4u7ps34pkxsqczjytzhkpvhv2bbkvtmujw7nydb.py
# Topologically Sorted Source Nodes: [batch_norm], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   batch_norm => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_per_fused__native_batch_norm_legit_2 = async_compile.triton('triton_per_fused__native_batch_norm_legit_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[16, 2],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 2
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*r1)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + (16*r1)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (x0 + (16*r1)), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp15 = tmp12[:, None]
    tmp16 = 16384.0
    tmp17 = tmp14 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(out_ptr2 + (x0), tmp20, xmask)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
    tl.store(out_ptr1 + (x0), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5h/c5h6ia34opuuxi62d6dkejio4tdeyvvyzxb3a3egfscqui5cqvbh.py
# Topologically Sorted Source Nodes: [batch_norm, x], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   batch_norm => add, add_1, mul, mul_1, rsqrt, sub, var_mean
#   x => relu
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %unsqueeze_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_3 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 16
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 16384.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qr/cqrddj2z5w457opl6srzuy4qd5rppfusuu4hz5zz5flksdkj34bs.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_3 => getitem_6, getitem_7
# Graph fragment:
#   %getitem_6 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_7 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_4 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_4(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = (xindex // 32)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pp/cppvhuquwkhuq735cq65shrngl5uhg2xfzscker75jvnsp64cr5d.py
# Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
# Graph fragment:
#   %convolution_3 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_6, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_5 = async_compile.triton('triton_poi_fused_convolution_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fv/cfv64mhapxe6uvgmszrxj264kq3oyfslkxbp2ihqg54xs7cferth.py
# Topologically Sorted Source Nodes: [batch_norm_3], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   batch_norm_3 => add_6, rsqrt_3, var_mean_3
# Graph fragment:
#   %var_mean_3 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_3, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_8, 1e-05), kwargs = {})
#   %rsqrt_3 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_6,), kwargs = {})
triton_red_fused__native_batch_norm_legit_6 = async_compile.triton('triton_red_fused__native_batch_norm_legit_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.reduction(
    size_hints=[32, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__native_batch_norm_legit_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused__native_batch_norm_legit_6(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 32
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex % 1024
        r2 = (rindex // 1024)
        tmp0 = tl.load(in_ptr0 + (r1 + (1024*x0) + (32768*r2)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = triton_helpers.welford_reduce(
            tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0
        )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(
        tmp2_mean, tmp2_m2, tmp2_weight, 1
    )
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp3, xmask)
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hc/chcxzggkbatw3f5npmxdfchl2kqqvbtlxh2cdenizmpjcbw5m5oj.py
# Topologically Sorted Source Nodes: [batch_norm_3, x_4], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   batch_norm_3 => add_6, add_7, mul_6, mul_7, rsqrt_3, sub_3, var_mean_3
#   x_4 => relu_3
# Graph fragment:
#   %var_mean_3 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_3, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_8, 1e-05), kwargs = {})
#   %rsqrt_3 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_6,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_3, %getitem_9), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %rsqrt_3), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %unsqueeze_13), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_7, %unsqueeze_15), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_7,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_7 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 32
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 4096.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6n/c6ndbntj2gw5jvp25obdm2vcnpwsvrxbz7zem2b5j3asm6h2y6xw.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_7 => getitem_14, getitem_15
# Graph fragment:
#   %getitem_14 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 0), kwargs = {})
#   %getitem_15 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_8 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_8(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/c5/cc5ffwguve3fnom45q3qvw66t3terecg76iezoyk7gevmytsbk2a.py
# Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_14, %primals_18, %primals_19, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_9 = async_compile.triton('triton_poi_fused_convolution_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_9(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/aa/caawzdy5yaezoju5wqnasg7o7yehk7sata37xfp66gzehdpweazv.py
# Topologically Sorted Source Nodes: [batch_norm_6], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   batch_norm_6 => add_12, rsqrt_6, var_mean_6
# Graph fragment:
#   %var_mean_6 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_6, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_16, 1e-05), kwargs = {})
#   %rsqrt_6 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_12,), kwargs = {})
triton_per_fused__native_batch_norm_legit_10 = async_compile.triton('triton_per_fused__native_batch_norm_legit_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[64, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_10', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_10(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 64
    XBLOCK: tl.constexpr = 1
    rnumel = 1024
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex % 256
    r2 = (rindex // 256)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (256*x0) + (16384*r2)), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + (x0), tmp18, None)
    tl.store(out_ptr0 + (x0), tmp8, None)
    tl.store(out_ptr1 + (x0), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/54/c54n6x3l2putw32m5h6kna3ut3tnlo6ljtp6uzkjbcsyq2gsnie2.py
# Topologically Sorted Source Nodes: [batch_norm_6, x_8], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   batch_norm_6 => add_12, add_13, mul_12, mul_13, rsqrt_6, sub_6, var_mean_6
#   x_8 => relu_6
# Graph fragment:
#   %var_mean_6 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_6, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_16, 1e-05), kwargs = {})
#   %rsqrt_6 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_12,), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_6, %getitem_17), kwargs = {})
#   %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %rsqrt_6), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_12, %unsqueeze_25), kwargs = {})
#   %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_13, %unsqueeze_27), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_13,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_11 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_11(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 64
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/n3/cn3jjvgjxbnhybyzxmomcqd6hneh5mwum3axqgnzynd7xo3co3dw.py
# Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_11 => getitem_22, getitem_23
# Graph fragment:
#   %getitem_22 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 0), kwargs = {})
#   %getitem_23 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_12 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_12(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (17 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/45/c45gx2vjeuqvj4uxxxzeh6awh7wi65zohiuwkycldrc73ioaugaf.py
# Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_9 => convolution_9
# Graph fragment:
#   %convolution_9 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_22, %primals_26, %primals_27, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_13 = async_compile.triton('triton_poi_fused_convolution_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hv/chvvykkytz5z5bfp4byfx7fsp4oe3xcftvmksvvczsw5vgqvkqgc.py
# Topologically Sorted Source Nodes: [batch_norm_9], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   batch_norm_9 => add_18, rsqrt_9, var_mean_9
# Graph fragment:
#   %var_mean_9 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_9, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_18 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_24, 1e-05), kwargs = {})
#   %rsqrt_9 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_18,), kwargs = {})
triton_per_fused__native_batch_norm_legit_14 = async_compile.triton('triton_per_fused__native_batch_norm_legit_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[128, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_14', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_14(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 128
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex % 64
    r2 = (rindex // 64)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0) + (8192*r2)), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + (x0), tmp18, None)
    tl.store(out_ptr0 + (x0), tmp8, None)
    tl.store(out_ptr1 + (x0), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/x5/cx5e5g57gfsa62ajoempe5jgd7vf6dco5aipsqyzriwiyrpgitad.py
# Topologically Sorted Source Nodes: [batch_norm_9, x_12], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   batch_norm_9 => add_18, add_19, mul_18, mul_19, rsqrt_9, sub_9, var_mean_9
#   x_12 => relu_9
# Graph fragment:
#   %var_mean_9 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_9, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_18 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_24, 1e-05), kwargs = {})
#   %rsqrt_9 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_18,), kwargs = {})
#   %sub_9 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_9, %getitem_25), kwargs = {})
#   %mul_18 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_9, %rsqrt_9), kwargs = {})
#   %mul_19 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_18, %unsqueeze_37), kwargs = {})
#   %add_19 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_19, %unsqueeze_39), kwargs = {})
#   %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_19,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_15 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_15(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 128
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/by/cby3qyk24hb4qgiwojv3km626rzzky2cst3j3yxa2kgbzlbuec75.py
# Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_15 => getitem_30, getitem_31
# Graph fragment:
#   %getitem_30 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 0), kwargs = {})
#   %getitem_31 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_16 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_16(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (9 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6s/c6sltu66ocnfvnfj5vfexjewbvf3mlv2sgjt5bxcpxbpasonqntj.py
# Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_12 => convolution_12
# Graph fragment:
#   %convolution_12 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_30, %primals_34, %primals_35, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_17 = async_compile.triton('triton_poi_fused_convolution_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_17', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_17(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7z/c7ztlblr7xqbsl6tq6t6kiubpv4m6uy77jsf5kmsrfcwpmmgxs4g.py
# Topologically Sorted Source Nodes: [batch_norm_12], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   batch_norm_12 => add_24, rsqrt_12, var_mean_12
# Graph fragment:
#   %var_mean_12 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_12, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_24 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_32, 1e-05), kwargs = {})
#   %rsqrt_12 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_24,), kwargs = {})
triton_per_fused__native_batch_norm_legit_18 = async_compile.triton('triton_per_fused__native_batch_norm_legit_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[256, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_18(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex % 16
    r2 = (rindex // 16)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0) + (4096*r2)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 64.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.store(out_ptr2 + (x0), tmp21, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
    tl.store(out_ptr1 + (x0), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/do/cdo367p5krf5643wl2lp7kzymjpyhh77xekuvgpc7j6phr2ztlpp.py
# Topologically Sorted Source Nodes: [batch_norm_12, x_16], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   batch_norm_12 => add_24, add_25, mul_24, mul_25, rsqrt_12, sub_12, var_mean_12
#   x_16 => relu_12
# Graph fragment:
#   %var_mean_12 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_12, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_24 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_32, 1e-05), kwargs = {})
#   %rsqrt_12 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_24,), kwargs = {})
#   %sub_12 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_12, %getitem_33), kwargs = {})
#   %mul_24 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_12, %rsqrt_12), kwargs = {})
#   %mul_25 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_24, %unsqueeze_49), kwargs = {})
#   %add_25 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_25, %unsqueeze_51), kwargs = {})
#   %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_25,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_19 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_19', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_19(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 256
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fb/cfbv5gghhz4fn3m364vqzbyheyz4srxwsliwwlu2irtjrulis32r.py
# Topologically Sorted Source Nodes: [batch_norm_14, x_18], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   batch_norm_14 => add_28, add_29, mul_28, mul_29, rsqrt_14, sub_14, var_mean_14
#   x_18 => relu_14
# Graph fragment:
#   %var_mean_14 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_14, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_28 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_36, 1e-05), kwargs = {})
#   %rsqrt_14 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_28,), kwargs = {})
#   %sub_14 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_14, %getitem_37), kwargs = {})
#   %mul_28 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_14, %rsqrt_14), kwargs = {})
#   %mul_29 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_28, %unsqueeze_49), kwargs = {})
#   %add_29 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_29, %unsqueeze_51), kwargs = {})
#   %relu_14 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_29,), kwargs = {})
#   %le_12 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_14, 0), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_20 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_20', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_20(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 256
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + (x3), tmp15, None)
    tl.store(out_ptr1 + (x3), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4t/c4tcufm7mpt6aqlqptbhq52kln32y2khmkkn64cwjycy7vjmvqxk.py
# Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_19 => add_30, add_31, convert_element_type, convert_element_type_1, iota, mul_30, mul_31
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul_30 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota, 1), kwargs = {})
#   %add_30 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_30, 0), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_30, torch.float32), kwargs = {})
#   %add_31 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
#   %mul_31 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_31, 0.5), kwargs = {})
#   %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_31, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_21 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_21', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_21(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5m/c5maacj3jtwj4eyy5dq4h3mtmlaeanbbzvyj3vfzn7jnmxm4boip.py
# Topologically Sorted Source Nodes: [x_19, x_20], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_19 => _unsafe_index
#   x_20 => constant_pad_nd
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_14, [None, None, %unsqueeze_60, %convert_element_type_1]), kwargs = {})
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%_unsafe_index, [0, 1, 0, 1], 0.0), kwargs = {})
triton_poi_fused__unsafe_index_constant_pad_nd_22 = async_compile.triton('triton_poi_fused__unsafe_index_constant_pad_nd_22', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_constant_pad_nd_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 9) % 9
    x0 = xindex % 9
    x2 = (xindex // 81)
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x1), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.full([XBLOCK], 4, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + (x0), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + (4*tmp10) + (16*x2)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x4), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3c/c3c5htiq2ehulnymtrosrcqsnvilvfompwrnwfho7hu2l3fdn2kd.py
# Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_22 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_11, %convolution_15], 1), kwargs = {})
triton_poi_fused_cat_23 = async_compile.triton('triton_poi_fused_cat_23', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_23', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_23(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 256
    x0 = xindex % 64
    x2 = (xindex // 16384)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (64*x1) + (8192*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 256, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (64*((-128) + x1)) + (8192*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-128) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ns/cnskur2c52mrxyiovhmdb572itchomuujsnufhu7y73ezbbzyxwg.py
# Topologically Sorted Source Nodes: [batch_norm_17, x_25], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   batch_norm_17 => add_38, add_39, mul_38, mul_39, rsqrt_17, sub_17, var_mean_17
#   x_25 => relu_17
# Graph fragment:
#   %var_mean_17 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_18, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_38 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_42, 1e-05), kwargs = {})
#   %rsqrt_17 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_38,), kwargs = {})
#   %sub_17 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_18, %getitem_43), kwargs = {})
#   %mul_38 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_17, %rsqrt_17), kwargs = {})
#   %mul_39 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_38, %unsqueeze_62), kwargs = {})
#   %add_39 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_39, %unsqueeze_64), kwargs = {})
#   %relu_17 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_39,), kwargs = {})
#   %le_9 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_17, 0), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_24 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_24', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_24(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 128
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + (x3), tmp15, None)
    tl.store(out_ptr1 + (x3), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qv/cqv6ykbkkdiqpwlgc4bg74ynroetpj5lejvledpcgvghal55jkf3.py
# Topologically Sorted Source Nodes: [x_26], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_26 => add_40, add_41, convert_element_type_4, convert_element_type_5, iota_2, mul_40, mul_41
# Graph fragment:
#   %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul_40 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {})
#   %add_40 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_40, 0), kwargs = {})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_40, torch.float32), kwargs = {})
#   %add_41 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {})
#   %mul_41 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_41, 0.5), kwargs = {})
#   %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_41, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_25 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_25', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_25', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_25(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pj/cpjjzxu5i7pi4ak5ygssr44dl5hvagmuhw3p36viajtikvpbslnk.py
# Topologically Sorted Source Nodes: [x_26, x_27], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_26 => _unsafe_index_1
#   x_27 => constant_pad_nd_1
# Graph fragment:
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_17, [None, None, %unsqueeze_73, %convert_element_type_5]), kwargs = {})
#   %constant_pad_nd_1 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%_unsafe_index_1, [0, 1, 0, 1], 0.0), kwargs = {})
triton_poi_fused__unsafe_index_constant_pad_nd_26 = async_compile.triton('triton_poi_fused__unsafe_index_constant_pad_nd_26', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_constant_pad_nd_26', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_26(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 17) % 17
    x0 = xindex % 17
    x2 = (xindex // 289)
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 16, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x1), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.full([XBLOCK], 8, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + (x0), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + (8*tmp10) + (64*x2)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x4), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bf/cbfcvtsp5ewbmbyb3aphiqhcctihzgp7xhxvq3sdtddtb6uj736z.py
# Topologically Sorted Source Nodes: [x_29], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_29 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_8, %convolution_19], 1), kwargs = {})
triton_poi_fused_cat_27 = async_compile.triton('triton_poi_fused_cat_27', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_27', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_27(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 256) % 128
    x0 = xindex % 256
    x2 = (xindex // 32768)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (256*x1) + (16384*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 128, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (256*((-64) + x1)) + (16384*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-64) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vr/cvr6qcfrkxtau4st6ykjg2srz5gtjhrrvqsryhjmik6xjowojnp6.py
# Topologically Sorted Source Nodes: [batch_norm_20, x_32], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   batch_norm_20 => add_48, add_49, mul_48, mul_49, rsqrt_20, sub_20, var_mean_20
#   x_32 => relu_20
# Graph fragment:
#   %var_mean_20 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_22, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_48 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_48, 1e-05), kwargs = {})
#   %rsqrt_20 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_48,), kwargs = {})
#   %sub_20 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_22, %getitem_49), kwargs = {})
#   %mul_48 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_20, %rsqrt_20), kwargs = {})
#   %mul_49 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_48, %unsqueeze_75), kwargs = {})
#   %add_49 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_49, %unsqueeze_77), kwargs = {})
#   %relu_20 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_49,), kwargs = {})
#   %le_6 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_20, 0), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_28 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_28', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_28', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_28(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 64
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + (x3), tmp15, None)
    tl.store(out_ptr1 + (x3), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/eo/ceohzfsaskcupvt37voxy2gaa7jcdrmg4w2724zb25xkzabdcbms.py
# Topologically Sorted Source Nodes: [x_33], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_33 => add_50, add_51, convert_element_type_8, convert_element_type_9, iota_4, mul_50, mul_51
# Graph fragment:
#   %iota_4 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul_50 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_4, 1), kwargs = {})
#   %add_50 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_50, 0), kwargs = {})
#   %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_50, torch.float32), kwargs = {})
#   %add_51 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {})
#   %mul_51 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_51, 0.5), kwargs = {})
#   %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_51, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_29 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_29', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_29', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_29(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qk/cqkd2lwnzhldswywxuzxeuiynsbclkc4nismtdmigjzaw4f2sfkp.py
# Topologically Sorted Source Nodes: [x_33, x_34], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_33 => _unsafe_index_2
#   x_34 => constant_pad_nd_2
# Graph fragment:
#   %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_20, [None, None, %unsqueeze_86, %convert_element_type_9]), kwargs = {})
#   %constant_pad_nd_2 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%_unsafe_index_2, [0, 1, 0, 1], 0.0), kwargs = {})
triton_poi_fused__unsafe_index_constant_pad_nd_30 = async_compile.triton('triton_poi_fused__unsafe_index_constant_pad_nd_30', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_constant_pad_nd_30', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_30(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 33) % 33
    x0 = xindex % 33
    x2 = (xindex // 1089)
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 32, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x1), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.full([XBLOCK], 16, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + (x0), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + (16*tmp10) + (256*x2)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x4), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sz/cszw3gpgw72pseinstaasv4upjsqap6k6e7vudpjzehclf6swm6x.py
# Topologically Sorted Source Nodes: [x_36], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_36 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_5, %convolution_23], 1), kwargs = {})
triton_poi_fused_cat_31 = async_compile.triton('triton_poi_fused_cat_31', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_31', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_31(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 1024) % 64
    x0 = xindex % 1024
    x2 = (xindex // 65536)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (1024*x1) + (32768*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 64, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (1024*((-32) + x1)) + (32768*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-32) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/a2/ca2dfmaijs5chw7u7ucb4hnvkcecdj3d3i247qkymwiw6kqtvtz5.py
# Topologically Sorted Source Nodes: [batch_norm_23, x_39], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   batch_norm_23 => add_58, add_59, mul_58, mul_59, rsqrt_23, sub_23, var_mean_23
#   x_39 => relu_23
# Graph fragment:
#   %var_mean_23 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%convolution_26, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_58 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_54, 1e-05), kwargs = {})
#   %rsqrt_23 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_58,), kwargs = {})
#   %sub_23 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_26, %getitem_55), kwargs = {})
#   %mul_58 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_23, %rsqrt_23), kwargs = {})
#   %mul_59 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_58, %unsqueeze_88), kwargs = {})
#   %add_59 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_59, %unsqueeze_90), kwargs = {})
#   %relu_23 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_59,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_23, 0), kwargs = {})
triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_32 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_32', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_32', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_32(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 32
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 4096.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + (x3), tmp15, None)
    tl.store(out_ptr1 + (x3), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wi/cwiczlgxwcgikfdob5rj3hbxy7jq3qgt74hx4ikp4oldudpj6g2j.py
# Topologically Sorted Source Nodes: [x_40], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_40 => add_60, add_61, convert_element_type_12, convert_element_type_13, iota_6, mul_60, mul_61
# Graph fragment:
#   %iota_6 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (64,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul_60 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_6, 1), kwargs = {})
#   %add_60 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_60, 0), kwargs = {})
#   %convert_element_type_12 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_60, torch.float32), kwargs = {})
#   %add_61 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_12, 0.0), kwargs = {})
#   %mul_61 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_61, 0.5), kwargs = {})
#   %convert_element_type_13 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_61, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_33 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_33', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_33', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_33(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3g/c3garej7jntewgdi5rzppp4n3dbeburc4c6h56gujysgt2xodeux.py
# Topologically Sorted Source Nodes: [x_40, x_41], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_40 => _unsafe_index_3
#   x_41 => constant_pad_nd_3
# Graph fragment:
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_23, [None, None, %unsqueeze_99, %convert_element_type_13]), kwargs = {})
#   %constant_pad_nd_3 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%_unsafe_index_3, [0, 1, 0, 1], 0.0), kwargs = {})
triton_poi_fused__unsafe_index_constant_pad_nd_34 = async_compile.triton('triton_poi_fused__unsafe_index_constant_pad_nd_34', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_constant_pad_nd_34', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_34(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 540800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 65) % 65
    x0 = xindex % 65
    x2 = (xindex // 4225)
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 64, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x1), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.full([XBLOCK], 32, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + (x0), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + (32*tmp10) + (1024*x2)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x4), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/n2/cn23k7urrmolzptbeulandvxbnd3cswv6mk7jwvizikioena3t75.py
# Topologically Sorted Source Nodes: [x_43], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_43 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_2, %convolution_27], 1), kwargs = {})
triton_poi_fused_cat_35 = async_compile.triton('triton_poi_fused_cat_35', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_35', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_35(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 32
    x0 = xindex % 4096
    x2 = (xindex // 131072)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (65536*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 32, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-16) + x1)) + (65536*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-16) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hl/chlanvf6deljvloy4dw3sabhgy2edb2wgppny7ntjxlkejy73qqx.py
# Topologically Sorted Source Nodes: [x10], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x10 => convolution_31
# Graph fragment:
#   %convolution_31 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_26, %primals_82, %primals_83, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_36 = async_compile.triton('triton_poi_fused_convolution_36', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_36', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_36(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73, primals_74, primals_75, primals_76, primals_77, primals_78, primals_79, primals_80, primals_81, primals_82, primals_83 = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (16, ), (1, ))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (16, ), (1, ))
    assert_size_stride(primals_10, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_11, (32, ), (1, ))
    assert_size_stride(primals_12, (32, ), (1, ))
    assert_size_stride(primals_13, (32, ), (1, ))
    assert_size_stride(primals_14, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_15, (32, ), (1, ))
    assert_size_stride(primals_16, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_17, (32, ), (1, ))
    assert_size_stride(primals_18, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_19, (64, ), (1, ))
    assert_size_stride(primals_20, (64, ), (1, ))
    assert_size_stride(primals_21, (64, ), (1, ))
    assert_size_stride(primals_22, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_23, (64, ), (1, ))
    assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_25, (64, ), (1, ))
    assert_size_stride(primals_26, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_27, (128, ), (1, ))
    assert_size_stride(primals_28, (128, ), (1, ))
    assert_size_stride(primals_29, (128, ), (1, ))
    assert_size_stride(primals_30, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_31, (128, ), (1, ))
    assert_size_stride(primals_32, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_33, (128, ), (1, ))
    assert_size_stride(primals_34, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_35, (256, ), (1, ))
    assert_size_stride(primals_36, (256, ), (1, ))
    assert_size_stride(primals_37, (256, ), (1, ))
    assert_size_stride(primals_38, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_39, (256, ), (1, ))
    assert_size_stride(primals_40, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_41, (256, ), (1, ))
    assert_size_stride(primals_42, (128, 256, 2, 2), (1024, 4, 2, 1))
    assert_size_stride(primals_43, (128, ), (1, ))
    assert_size_stride(primals_44, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_45, (128, ), (1, ))
    assert_size_stride(primals_46, (128, ), (1, ))
    assert_size_stride(primals_47, (128, ), (1, ))
    assert_size_stride(primals_48, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_49, (128, ), (1, ))
    assert_size_stride(primals_50, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_51, (128, ), (1, ))
    assert_size_stride(primals_52, (64, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_53, (64, ), (1, ))
    assert_size_stride(primals_54, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_55, (64, ), (1, ))
    assert_size_stride(primals_56, (64, ), (1, ))
    assert_size_stride(primals_57, (64, ), (1, ))
    assert_size_stride(primals_58, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_59, (64, ), (1, ))
    assert_size_stride(primals_60, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_61, (64, ), (1, ))
    assert_size_stride(primals_62, (32, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_63, (32, ), (1, ))
    assert_size_stride(primals_64, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_65, (32, ), (1, ))
    assert_size_stride(primals_66, (32, ), (1, ))
    assert_size_stride(primals_67, (32, ), (1, ))
    assert_size_stride(primals_68, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_69, (32, ), (1, ))
    assert_size_stride(primals_70, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_71, (32, ), (1, ))
    assert_size_stride(primals_72, (16, 32, 2, 2), (128, 4, 2, 1))
    assert_size_stride(primals_73, (16, ), (1, ))
    assert_size_stride(primals_74, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_75, (16, ), (1, ))
    assert_size_stride(primals_76, (16, ), (1, ))
    assert_size_stride(primals_77, (16, ), (1, ))
    assert_size_stride(primals_78, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_79, (16, ), (1, ))
    assert_size_stride(primals_80, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_81, (16, ), (1, ))
    assert_size_stride(primals_82, (1, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_83, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 262144, grid=grid(262144), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16), torch.float32)
        buf3 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16), torch.float32)
        buf4 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_1.run(buf1, buf2, buf3, buf4, 32, 8192, grid=grid(32), stream=stream0)
        buf5 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf8 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_2.run(buf2, buf3, buf4, buf5, buf6, buf8, 16, 2, grid=grid(16), stream=stream0)
        buf9 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm, x], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_3.run(buf1, buf5, buf6, primals_4, primals_5, buf9, 262144, grid=grid(262144), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf11, primals_7, 262144, grid=grid(262144), stream=stream0)
        del primals_7
        buf12 = buf4; del buf4  # reuse
        buf13 = buf3; del buf3  # reuse
        buf14 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_1], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_1.run(buf11, buf12, buf13, buf14, 32, 8192, grid=grid(32), stream=stream0)
        buf15 = buf6; del buf6  # reuse
        buf16 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf18 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_1], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_2.run(buf12, buf13, buf14, buf15, buf16, buf18, 16, 2, grid=grid(16), stream=stream0)
        buf19 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_1, x_1], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_3.run(buf11, buf15, buf16, primals_4, primals_5, buf19, 262144, grid=grid(262144), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf21, primals_9, 262144, grid=grid(262144), stream=stream0)
        del primals_9
        buf22 = buf14; del buf14  # reuse
        buf23 = buf13; del buf13  # reuse
        buf24 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_2], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_1.run(buf21, buf22, buf23, buf24, 32, 8192, grid=grid(32), stream=stream0)
        buf25 = buf16; del buf16  # reuse
        buf26 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf28 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_2], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_2.run(buf22, buf23, buf24, buf25, buf26, buf28, 16, 2, grid=grid(16), stream=stream0)
        buf29 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_2, x_2], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_3.run(buf21, buf25, buf26, primals_4, primals_5, buf29, 262144, grid=grid(262144), stream=stream0)
        del primals_5
        buf30 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.float32)
        buf31 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_4.run(buf29, buf30, buf31, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf30, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf33 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf33, primals_11, 131072, grid=grid(131072), stream=stream0)
        del primals_11
        buf34 = reinterpret_tensor(buf24, (1, 32, 1, 1), (32, 1, 32, 32), 0); del buf24  # reuse
        buf35 = reinterpret_tensor(buf23, (1, 32, 1, 1), (32, 1, 32, 32), 0); del buf23  # reuse
        buf37 = reinterpret_tensor(buf22, (1, 32, 1, 1), (32, 1, 32, 32), 0); del buf22  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_3], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_6.run(buf33, buf34, buf35, buf37, 32, 4096, grid=grid(32), stream=stream0)
        buf38 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_3, x_4], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_7.run(buf33, buf34, buf35, primals_12, primals_13, buf38, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf39 = extern_kernels.convolution(buf38, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf40 = buf39; del buf39  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf40, primals_15, 131072, grid=grid(131072), stream=stream0)
        del primals_15
        buf41 = buf35; del buf35  # reuse
        buf42 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        buf44 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_4], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_6.run(buf40, buf41, buf42, buf44, 32, 4096, grid=grid(32), stream=stream0)
        buf45 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_4, x_5], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_7.run(buf40, buf41, buf42, primals_12, primals_13, buf45, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf47 = buf46; del buf46  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf47, primals_17, 131072, grid=grid(131072), stream=stream0)
        del primals_17
        buf48 = buf42; del buf42  # reuse
        buf49 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        buf51 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_5], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_6.run(buf47, buf48, buf49, buf51, 32, 4096, grid=grid(32), stream=stream0)
        buf52 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_5, x_6], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_7.run(buf47, buf48, buf49, primals_12, primals_13, buf52, 131072, grid=grid(131072), stream=stream0)
        del primals_13
        buf53 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.float32)
        buf54 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_8.run(buf52, buf53, buf54, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf55 = extern_kernels.convolution(buf53, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf55, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf56 = buf55; del buf55  # reuse
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_9.run(buf56, primals_19, 65536, grid=grid(65536), stream=stream0)
        del primals_19
        buf57 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        buf58 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        buf60 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_6], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_10.run(buf56, buf57, buf58, buf60, 64, 1024, grid=grid(64), stream=stream0)
        buf61 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_6, x_8], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_11.run(buf56, buf57, buf58, primals_20, primals_21, buf61, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf62 = extern_kernels.convolution(buf61, primals_22, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf62, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf63 = buf62; del buf62  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_9.run(buf63, primals_23, 65536, grid=grid(65536), stream=stream0)
        del primals_23
        buf64 = buf58; del buf58  # reuse
        buf65 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        buf67 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_7], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_10.run(buf63, buf64, buf65, buf67, 64, 1024, grid=grid(64), stream=stream0)
        buf68 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_7, x_9], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_11.run(buf63, buf64, buf65, primals_20, primals_21, buf68, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf69 = extern_kernels.convolution(buf68, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf70 = buf69; del buf69  # reuse
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_9.run(buf70, primals_25, 65536, grid=grid(65536), stream=stream0)
        del primals_25
        buf71 = buf65; del buf65  # reuse
        buf72 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        buf74 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_8], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_10.run(buf70, buf71, buf72, buf74, 64, 1024, grid=grid(64), stream=stream0)
        buf75 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_8, x_10], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_11.run(buf70, buf71, buf72, primals_20, primals_21, buf75, 65536, grid=grid(65536), stream=stream0)
        del primals_21
        buf76 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.float32)
        buf77 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_12.run(buf75, buf76, buf77, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf78 = extern_kernels.convolution(buf76, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf79 = buf78; del buf78  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf79, primals_27, 32768, grid=grid(32768), stream=stream0)
        del primals_27
        buf80 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf81 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf83 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_9], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_14.run(buf79, buf80, buf81, buf83, 128, 256, grid=grid(128), stream=stream0)
        buf84 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_9, x_12], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_15.run(buf79, buf80, buf81, primals_28, primals_29, buf84, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf85 = extern_kernels.convolution(buf84, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf85, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf86 = buf85; del buf85  # reuse
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf86, primals_31, 32768, grid=grid(32768), stream=stream0)
        del primals_31
        buf87 = buf81; del buf81  # reuse
        buf88 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf90 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_10], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_14.run(buf86, buf87, buf88, buf90, 128, 256, grid=grid(128), stream=stream0)
        buf91 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_10, x_13], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_15.run(buf86, buf87, buf88, primals_28, primals_29, buf91, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf92 = extern_kernels.convolution(buf91, primals_32, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf92, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf93 = buf92; del buf92  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf93, primals_33, 32768, grid=grid(32768), stream=stream0)
        del primals_33
        buf94 = buf88; del buf88  # reuse
        buf95 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf97 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_11], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_14.run(buf93, buf94, buf95, buf97, 128, 256, grid=grid(128), stream=stream0)
        buf98 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_11, x_14], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_15.run(buf93, buf94, buf95, primals_28, primals_29, buf98, 32768, grid=grid(32768), stream=stream0)
        del primals_29
        buf99 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        buf100 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_16.run(buf98, buf99, buf100, 8192, grid=grid(8192), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf101 = extern_kernels.convolution(buf99, primals_34, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf102 = buf101; del buf101  # reuse
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_17.run(buf102, primals_35, 16384, grid=grid(16384), stream=stream0)
        del primals_35
        buf103 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        buf104 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        buf106 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_12], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_18.run(buf102, buf103, buf104, buf106, 256, 64, grid=grid(256), stream=stream0)
        buf107 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_12, x_16], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_19.run(buf102, buf103, buf104, primals_36, primals_37, buf107, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf108 = extern_kernels.convolution(buf107, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf109 = buf108; del buf108  # reuse
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_17.run(buf109, primals_39, 16384, grid=grid(16384), stream=stream0)
        del primals_39
        buf110 = buf104; del buf104  # reuse
        buf111 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        buf113 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_13], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_18.run(buf109, buf110, buf111, buf113, 256, 64, grid=grid(256), stream=stream0)
        buf114 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_13, x_17], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_19.run(buf109, buf110, buf111, primals_36, primals_37, buf114, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        buf115 = extern_kernels.convolution(buf114, primals_40, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf115, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf116 = buf115; del buf115  # reuse
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_17.run(buf116, primals_41, 16384, grid=grid(16384), stream=stream0)
        del primals_41
        buf117 = buf111; del buf111  # reuse
        buf118 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        buf120 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_14], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_18.run(buf116, buf117, buf118, buf120, 256, 64, grid=grid(256), stream=stream0)
        buf121 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        buf236 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [batch_norm_14, x_18], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_20.run(buf116, buf117, buf118, primals_36, primals_37, buf121, buf236, 16384, grid=grid(16384), stream=stream0)
        del buf118
        del primals_37
        buf122 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_21.run(buf122, 8, grid=grid(8), stream=stream0)
        buf123 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_19, x_20], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
        triton_poi_fused__unsafe_index_constant_pad_nd_22.run(buf122, buf121, buf123, 82944, grid=grid(82944), stream=stream0)
        del buf121
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.convolution]
        buf124 = extern_kernels.convolution(buf123, primals_42, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf124, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf125 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.cat]
        triton_poi_fused_cat_23.run(buf98, buf124, primals_43, buf125, 65536, grid=grid(65536), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        buf126 = extern_kernels.convolution(buf125, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf126, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf127 = buf126; del buf126  # reuse
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf127, primals_45, 32768, grid=grid(32768), stream=stream0)
        del primals_45
        buf128 = buf95; del buf95  # reuse
        buf129 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf131 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_15], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_14.run(buf127, buf128, buf129, buf131, 128, 256, grid=grid(128), stream=stream0)
        buf132 = buf124; del buf124  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_15, x_23], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_15.run(buf127, buf128, buf129, primals_46, primals_47, buf132, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        buf133 = extern_kernels.convolution(buf132, primals_48, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf133, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf134 = buf133; del buf133  # reuse
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf134, primals_49, 32768, grid=grid(32768), stream=stream0)
        del primals_49
        buf135 = buf129; del buf129  # reuse
        buf136 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf138 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_16], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_14.run(buf134, buf135, buf136, buf138, 128, 256, grid=grid(128), stream=stream0)
        buf139 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_16, x_24], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_15.run(buf134, buf135, buf136, primals_46, primals_47, buf139, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_18], Original ATen: [aten.convolution]
        buf140 = extern_kernels.convolution(buf139, primals_50, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf140, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf141 = buf140; del buf140  # reuse
        # Topologically Sorted Source Nodes: [conv2d_18], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf141, primals_51, 32768, grid=grid(32768), stream=stream0)
        del primals_51
        buf142 = buf136; del buf136  # reuse
        buf143 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf145 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_17], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_14.run(buf141, buf142, buf143, buf145, 128, 256, grid=grid(128), stream=stream0)
        buf146 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        buf235 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [batch_norm_17, x_25], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_24.run(buf141, buf142, buf143, primals_46, primals_47, buf146, buf235, 32768, grid=grid(32768), stream=stream0)
        del buf143
        del primals_47
        buf147 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_26], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_25.run(buf147, 16, grid=grid(16), stream=stream0)
        buf148 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_26, x_27], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
        triton_poi_fused__unsafe_index_constant_pad_nd_26.run(buf147, buf146, buf148, 147968, grid=grid(147968), stream=stream0)
        del buf146
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.convolution]
        buf149 = extern_kernels.convolution(buf148, primals_52, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf149, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf150 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_29], Original ATen: [aten.cat]
        triton_poi_fused_cat_27.run(buf75, buf149, primals_53, buf150, 131072, grid=grid(131072), stream=stream0)
        del primals_53
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        buf151 = extern_kernels.convolution(buf150, primals_54, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf151, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf152 = buf151; del buf151  # reuse
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_9.run(buf152, primals_55, 65536, grid=grid(65536), stream=stream0)
        del primals_55
        buf153 = buf72; del buf72  # reuse
        buf154 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        buf156 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_18], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_10.run(buf152, buf153, buf154, buf156, 64, 1024, grid=grid(64), stream=stream0)
        buf157 = buf149; del buf149  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_18, x_30], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_11.run(buf152, buf153, buf154, primals_56, primals_57, buf157, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        buf158 = extern_kernels.convolution(buf157, primals_58, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf158, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf159 = buf158; del buf158  # reuse
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_9.run(buf159, primals_59, 65536, grid=grid(65536), stream=stream0)
        del primals_59
        buf160 = buf154; del buf154  # reuse
        buf161 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        buf163 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_19], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_10.run(buf159, buf160, buf161, buf163, 64, 1024, grid=grid(64), stream=stream0)
        buf164 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_19, x_31], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_11.run(buf159, buf160, buf161, primals_56, primals_57, buf164, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        buf165 = extern_kernels.convolution(buf164, primals_60, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf165, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf166 = buf165; del buf165  # reuse
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_9.run(buf166, primals_61, 65536, grid=grid(65536), stream=stream0)
        del primals_61
        buf167 = buf161; del buf161  # reuse
        buf168 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        buf170 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_20], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_10.run(buf166, buf167, buf168, buf170, 64, 1024, grid=grid(64), stream=stream0)
        buf171 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.float32)
        buf234 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [batch_norm_20, x_32], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_28.run(buf166, buf167, buf168, primals_56, primals_57, buf171, buf234, 65536, grid=grid(65536), stream=stream0)
        del buf168
        del primals_57
        buf172 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_33], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_29.run(buf172, 32, grid=grid(32), stream=stream0)
        buf173 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_33, x_34], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
        triton_poi_fused__unsafe_index_constant_pad_nd_30.run(buf172, buf171, buf173, 278784, grid=grid(278784), stream=stream0)
        del buf171
        # Topologically Sorted Source Nodes: [x_35], Original ATen: [aten.convolution]
        buf174 = extern_kernels.convolution(buf173, primals_62, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf174, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf175 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_36], Original ATen: [aten.cat]
        triton_poi_fused_cat_31.run(buf52, buf174, primals_63, buf175, 262144, grid=grid(262144), stream=stream0)
        del primals_63
        # Topologically Sorted Source Nodes: [conv2d_24], Original ATen: [aten.convolution]
        buf176 = extern_kernels.convolution(buf175, primals_64, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf176, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf177 = buf176; del buf176  # reuse
        # Topologically Sorted Source Nodes: [conv2d_24], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf177, primals_65, 131072, grid=grid(131072), stream=stream0)
        del primals_65
        buf178 = buf49; del buf49  # reuse
        buf179 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        buf181 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_21], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_6.run(buf177, buf178, buf179, buf181, 32, 4096, grid=grid(32), stream=stream0)
        buf182 = buf174; del buf174  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_21, x_37], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_7.run(buf177, buf178, buf179, primals_66, primals_67, buf182, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_25], Original ATen: [aten.convolution]
        buf183 = extern_kernels.convolution(buf182, primals_68, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf183, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf184 = buf183; del buf183  # reuse
        # Topologically Sorted Source Nodes: [conv2d_25], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf184, primals_69, 131072, grid=grid(131072), stream=stream0)
        del primals_69
        buf185 = buf179; del buf179  # reuse
        buf186 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        buf188 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_22], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_6.run(buf184, buf185, buf186, buf188, 32, 4096, grid=grid(32), stream=stream0)
        buf189 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_22, x_38], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_7.run(buf184, buf185, buf186, primals_66, primals_67, buf189, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_26], Original ATen: [aten.convolution]
        buf190 = extern_kernels.convolution(buf189, primals_70, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf190, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf191 = buf190; del buf190  # reuse
        # Topologically Sorted Source Nodes: [conv2d_26], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf191, primals_71, 131072, grid=grid(131072), stream=stream0)
        del primals_71
        buf192 = buf186; del buf186  # reuse
        buf193 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        buf195 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_23], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_6.run(buf191, buf192, buf193, buf195, 32, 4096, grid=grid(32), stream=stream0)
        buf196 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.float32)
        buf233 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [batch_norm_23, x_39], Original ATen: [aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_32.run(buf191, buf192, buf193, primals_66, primals_67, buf196, buf233, 131072, grid=grid(131072), stream=stream0)
        del primals_67
        buf197 = empty_strided_cuda((64, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_40], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_33.run(buf197, 64, grid=grid(64), stream=stream0)
        buf198 = empty_strided_cuda((4, 32, 65, 65), (135200, 4225, 65, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_40, x_41], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
        triton_poi_fused__unsafe_index_constant_pad_nd_34.run(buf197, buf196, buf198, 540800, grid=grid(540800), stream=stream0)
        del buf196
        # Topologically Sorted Source Nodes: [x_42], Original ATen: [aten.convolution]
        buf199 = extern_kernels.convolution(buf198, primals_72, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf199, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf200 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_43], Original ATen: [aten.cat]
        triton_poi_fused_cat_35.run(buf29, buf199, primals_73, buf200, 524288, grid=grid(524288), stream=stream0)
        del primals_73
        # Topologically Sorted Source Nodes: [conv2d_28], Original ATen: [aten.convolution]
        buf201 = extern_kernels.convolution(buf200, primals_74, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf201, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf202 = buf201; del buf201  # reuse
        # Topologically Sorted Source Nodes: [conv2d_28], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf202, primals_75, 262144, grid=grid(262144), stream=stream0)
        del primals_75
        buf203 = reinterpret_tensor(buf193, (1, 16, 1, 1, 2), (32, 1, 32, 32, 16), 0); del buf193  # reuse
        buf204 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16), torch.float32)
        buf205 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_24], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_1.run(buf202, buf203, buf204, buf205, 32, 8192, grid=grid(32), stream=stream0)
        buf206 = buf26; del buf26  # reuse
        buf207 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf209 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_24], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_2.run(buf203, buf204, buf205, buf206, buf207, buf209, 16, 2, grid=grid(16), stream=stream0)
        buf210 = buf199; del buf199  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_24, x_44], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_3.run(buf202, buf206, buf207, primals_76, primals_77, buf210, 262144, grid=grid(262144), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_29], Original ATen: [aten.convolution]
        buf211 = extern_kernels.convolution(buf210, primals_78, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf211, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf212 = buf211; del buf211  # reuse
        # Topologically Sorted Source Nodes: [conv2d_29], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf212, primals_79, 262144, grid=grid(262144), stream=stream0)
        del primals_79
        buf213 = buf205; del buf205  # reuse
        buf214 = buf204; del buf204  # reuse
        buf215 = buf203; del buf203  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_25], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_1.run(buf212, buf213, buf214, buf215, 32, 8192, grid=grid(32), stream=stream0)
        buf216 = buf207; del buf207  # reuse
        buf217 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf219 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_25], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_2.run(buf213, buf214, buf215, buf216, buf217, buf219, 16, 2, grid=grid(16), stream=stream0)
        buf220 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_25, x_45], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_3.run(buf212, buf216, buf217, primals_76, primals_77, buf220, 262144, grid=grid(262144), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_30], Original ATen: [aten.convolution]
        buf221 = extern_kernels.convolution(buf220, primals_80, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf221, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf222 = buf221; del buf221  # reuse
        # Topologically Sorted Source Nodes: [conv2d_30], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf222, primals_81, 262144, grid=grid(262144), stream=stream0)
        del primals_81
        buf223 = buf215; del buf215  # reuse
        buf224 = buf214; del buf214  # reuse
        buf225 = buf213; del buf213  # reuse
        # Topologically Sorted Source Nodes: [batch_norm_26], Original ATen: [aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_1.run(buf222, buf223, buf224, buf225, 32, 8192, grid=grid(32), stream=stream0)
        buf226 = buf217; del buf217  # reuse
        buf227 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf229 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_26], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_2.run(buf223, buf224, buf225, buf226, buf227, buf229, 16, 2, grid=grid(16), stream=stream0)
        del buf223
        del buf224
        del buf225
        buf230 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm_26, x_46], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_poi_fused__native_batch_norm_legit_relu_3.run(buf222, buf226, buf227, primals_76, primals_77, buf230, 262144, grid=grid(262144), stream=stream0)
        del buf227
        del primals_77
        # Topologically Sorted Source Nodes: [x10], Original ATen: [aten.convolution]
        buf231 = extern_kernels.convolution(buf230, primals_82, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf231, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf232 = buf231; del buf231  # reuse
        # Topologically Sorted Source Nodes: [x10], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_36.run(buf232, primals_83, 16384, grid=grid(16384), stream=stream0)
        del primals_83
    return (buf232, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, primals_28, primals_30, primals_32, primals_34, primals_36, primals_38, primals_40, primals_42, primals_44, primals_46, primals_48, primals_50, primals_52, primals_54, primals_56, primals_58, primals_60, primals_62, primals_64, primals_66, primals_68, primals_70, primals_72, primals_74, primals_76, primals_78, primals_80, primals_82, buf1, reinterpret_tensor(buf8, (16, ), (1, ), 0), buf9, buf11, reinterpret_tensor(buf18, (16, ), (1, ), 0), buf19, buf21, reinterpret_tensor(buf28, (16, ), (1, ), 0), buf29, buf30, buf31, buf33, reinterpret_tensor(buf37, (32, ), (1, ), 0), buf38, buf40, reinterpret_tensor(buf44, (32, ), (1, ), 0), buf45, buf47, reinterpret_tensor(buf51, (32, ), (1, ), 0), buf52, buf53, buf54, buf56, reinterpret_tensor(buf60, (64, ), (1, ), 0), buf61, buf63, reinterpret_tensor(buf67, (64, ), (1, ), 0), buf68, buf70, reinterpret_tensor(buf74, (64, ), (1, ), 0), buf75, buf76, buf77, buf79, reinterpret_tensor(buf83, (128, ), (1, ), 0), buf84, buf86, reinterpret_tensor(buf90, (128, ), (1, ), 0), buf91, buf93, reinterpret_tensor(buf97, (128, ), (1, ), 0), buf98, buf99, buf100, buf102, reinterpret_tensor(buf106, (256, ), (1, ), 0), buf107, buf109, reinterpret_tensor(buf113, (256, ), (1, ), 0), buf114, buf116, reinterpret_tensor(buf120, (256, ), (1, ), 0), buf122, buf123, buf125, buf127, reinterpret_tensor(buf131, (128, ), (1, ), 0), buf132, buf134, reinterpret_tensor(buf138, (128, ), (1, ), 0), buf139, buf141, reinterpret_tensor(buf145, (128, ), (1, ), 0), buf147, buf148, buf150, buf152, reinterpret_tensor(buf156, (64, ), (1, ), 0), buf157, buf159, reinterpret_tensor(buf163, (64, ), (1, ), 0), buf164, buf166, reinterpret_tensor(buf170, (64, ), (1, ), 0), buf172, buf173, buf175, buf177, reinterpret_tensor(buf181, (32, ), (1, ), 0), buf182, buf184, reinterpret_tensor(buf188, (32, ), (1, ), 0), buf189, buf191, reinterpret_tensor(buf195, (32, ), (1, ), 0), buf197, buf198, buf200, buf202, reinterpret_tensor(buf209, (16, ), (1, ), 0), buf210, buf212, reinterpret_tensor(buf219, (16, ), (1, ), 0), buf220, buf222, reinterpret_tensor(buf229, (16, ), (1, ), 0), buf230, reinterpret_tensor(buf226, (1, 16, 1, 1), (16, 1, 1, 1), 0), reinterpret_tensor(buf216, (1, 16, 1, 1), (16, 1, 1, 1), 0), reinterpret_tensor(buf206, (1, 16, 1, 1), (16, 1, 1, 1), 0), buf233, reinterpret_tensor(buf192, (1, 32, 1, 1), (32, 1, 1, 1), 0), reinterpret_tensor(buf185, (1, 32, 1, 1), (32, 1, 1, 1), 0), reinterpret_tensor(buf178, (1, 32, 1, 1), (32, 1, 1, 1), 0), buf234, reinterpret_tensor(buf167, (1, 64, 1, 1), (64, 1, 1, 1), 0), reinterpret_tensor(buf160, (1, 64, 1, 1), (64, 1, 1, 1), 0), reinterpret_tensor(buf153, (1, 64, 1, 1), (64, 1, 1, 1), 0), buf235, reinterpret_tensor(buf142, (1, 128, 1, 1), (128, 1, 1, 1), 0), reinterpret_tensor(buf135, (1, 128, 1, 1), (128, 1, 1, 1), 0), reinterpret_tensor(buf128, (1, 128, 1, 1), (128, 1, 1, 1), 0), buf236, reinterpret_tensor(buf117, (1, 256, 1, 1), (256, 1, 1, 1), 0), reinterpret_tensor(buf110, (1, 256, 1, 1), (256, 1, 1, 1), 0), reinterpret_tensor(buf103, (1, 256, 1, 1), (256, 1, 1, 1), 0), reinterpret_tensor(buf94, (1, 128, 1, 1), (128, 1, 1, 1), 0), reinterpret_tensor(buf87, (1, 128, 1, 1), (128, 1, 1, 1), 0), reinterpret_tensor(buf80, (1, 128, 1, 1), (128, 1, 1, 1), 0), reinterpret_tensor(buf71, (1, 64, 1, 1), (64, 1, 1, 1), 0), reinterpret_tensor(buf64, (1, 64, 1, 1), (64, 1, 1, 1), 0), reinterpret_tensor(buf57, (1, 64, 1, 1), (64, 1, 1, 1), 0), reinterpret_tensor(buf48, (1, 32, 1, 1), (32, 1, 1, 1), 0), reinterpret_tensor(buf41, (1, 32, 1, 1), (32, 1, 1, 1), 0), reinterpret_tensor(buf34, (1, 32, 1, 1), (32, 1, 1, 1), 0), reinterpret_tensor(buf25, (1, 16, 1, 1), (16, 1, 1, 1), 0), reinterpret_tensor(buf15, (1, 16, 1, 1), (16, 1, 1, 1), 0), reinterpret_tensor(buf5, (1, 16, 1, 1), (16, 1, 1, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((16, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((32, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((128, 256, 2, 2), (1024, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((64, 128, 2, 2), (512, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_55 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_56 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_57 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_58 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_59 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_60 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_61 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_62 = rand_strided((32, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_63 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_64 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_65 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_66 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_67 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_68 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_69 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_70 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_71 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_72 = rand_strided((16, 32, 2, 2), (128, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_73 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_74 = rand_strided((16, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_75 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_76 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_77 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_78 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_79 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_80 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_81 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_82 = rand_strided((1, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_83 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73, primals_74, primals_75, primals_76, primals_77, primals_78, primals_79, primals_80, primals_81, primals_82, primals_83])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *


def conv3x3(in_channels, out_channels):
    return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, bias=True)


def maxpool2x2():
    return nn.MaxPool2d(kernel_size=2, stride=2, padding=0)


def concat(xh, xv):
    return torch.cat([xh, xv], dim=1)


class UpConv2x2(nn.Module):

    def __init__(self, channels):
        super(UpConv2x2, self).__init__()
        self.conv = nn.Conv2d(channels, channels // 2, kernel_size=2,
            stride=1, padding=0, bias=True)

    def forward(self, x):
        x = F.interpolate(x, scale_factor=2, mode='nearest')
        x = F.pad(x, (0, 1, 0, 1))
        x = self.conv(x)
        return x


class ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(ConvBlock, self).__init__()
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

    def forward(self, x):
        x = F.relu(self.norm(self.conv1(x)))
        x = F.relu(self.norm(self.conv2(x)))
        x = F.relu(self.norm(self.conv3(x)))
        return x


class DownConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(DownConvBlock, self).__init__()
        self.maxpool = maxpool2x2()
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

    def forward(self, x):
        x = self.maxpool(x)
        x = F.relu(self.norm(self.conv1(x)))
        x = F.relu(self.norm(self.conv2(x)))
        x = F.relu(self.norm(self.conv3(x)))
        return x


class UpConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(UpConvBlock, self).__init__()
        self.upconv = UpConv2x2(in_channels)
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

    def forward(self, xh, xv):
        """
        Args:
            xh: torch Variable, activations from same resolution feature maps (gray arrow in diagram)
            xv: torch Variable, activations from lower resolution feature maps (green arrow in diagram)
        """
        xv = self.upconv(xv)
        x = concat(xh, xv)
        x = F.relu(self.norm(self.conv1(x)))
        x = F.relu(self.norm(self.conv2(x)))
        x = F.relu(self.norm(self.conv3(x)))
        return x


class UNet(nn.Module):

    def __init__(self):
        super(UNet, self).__init__()
        fs = [16, 32, 64, 128, 256]
        self.conv_in = ConvBlock(1, fs[0])
        self.dconv1 = DownConvBlock(fs[0], fs[1])
        self.dconv2 = DownConvBlock(fs[1], fs[2])
        self.dconv3 = DownConvBlock(fs[2], fs[3])
        self.dconv4 = DownConvBlock(fs[3], fs[4])
        self.uconv1 = UpConvBlock(fs[4], fs[3])
        self.uconv2 = UpConvBlock(fs[3], fs[2])
        self.uconv3 = UpConvBlock(fs[2], fs[1])
        self.uconv4 = UpConvBlock(fs[1], fs[0])
        self.conv_out = conv3x3(fs[0], 1)
        self._initialize_weights()

    def forward(self, x):
        x1 = self.conv_in(x)
        x2 = self.dconv1(x1)
        x3 = self.dconv2(x2)
        x4 = self.dconv3(x3)
        x5 = self.dconv4(x4)
        x6 = self.uconv1(x4, x5)
        x7 = self.uconv2(x3, x6)
        x8 = self.uconv3(x2, x7)
        x9 = self.uconv4(x1, x8)
        x10 = self.conv_out(x9)
        return x10

    def _initialize_weights(self):
        conv_modules = [m for m in self.modules() if isinstance(m, nn.Conv2d)]
        for m in conv_modules:
            n = m.weight.shape[1] * m.weight.shape[2] * m.weight.shape[3]
            m.weight.data.normal_(0, np.sqrt(2.0 / n))
            m.bias.data.zero_()


def get_inputs():
    return [torch.rand([4, 1, 64, 64])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_red_fused__native_batch_norm_legit_1(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 32
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 16
    x1 = xindex // 16
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (4096 * x0 + 65536 * (r2 // 4096) + 131072 *
            x1 + r2 % 4096), rmask & xmask, eviction_policy='evict_first',
            other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tl.store(out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr1 + x3, tmp3, xmask)
    tl.store(out_ptr2 + x3, tmp4, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * r1), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * r1), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (x0 + 16 * r1), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp12[:, None]
    tmp16 = 16384.0
    tmp17 = tmp14 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(out_ptr2 + x0, tmp20, xmask)
    tl.store(out_ptr0 + x0, tmp13, xmask)
    tl.store(out_ptr1 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_3(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 16
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 16384.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_4(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_red_fused__native_batch_norm_legit_6(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 32
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex % 1024
        r2 = rindex // 1024
        tmp0 = tl.load(in_ptr0 + (r1 + 1024 * x0 + 32768 * r2), rmask &
            xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_7(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 4096.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_8(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_9(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_10(in_ptr0, out_ptr0,
    out_ptr1, out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex % 256
    r2 = rindex // 256
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 256 * x0 + 16384 * r2), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x0, tmp18, None)
    tl.store(out_ptr0 + x0, tmp8, None)
    tl.store(out_ptr1 + x0, tmp13, None)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_11(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_12(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 32 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 32 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + 2 * x0 + 32 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (17 + 2 * x0 + 32 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_14(in_ptr0, out_ptr0,
    out_ptr1, out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex % 64
    r2 = rindex // 64
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0 + 8192 * r2), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x0, tmp18, None)
    tl.store(out_ptr0 + x0, tmp8, None)
    tl.store(out_ptr1 + x0, tmp13, None)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_15(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 128
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_16(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (9 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_17(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_18(in_ptr0, out_ptr0,
    out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex % 16
    r2 = rindex // 16
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0 + 4096 * r2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 64.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.store(out_ptr2 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_19(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 256
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_20(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 256
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + x3, tmp15, None)
    tl.store(out_ptr1 + x3, tmp17, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_21(out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_22(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 9 % 9
    x0 = xindex % 9
    x2 = xindex // 81
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + x1, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tl.full([XBLOCK], 4, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + x0, tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + 4 * tmp10 + 16 * x2), tmp5 & xmask,
        eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + x4, tmp15, xmask)


@triton.jit
def triton_poi_fused_cat_23(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 64 % 256
    x0 = xindex % 64
    x2 = xindex // 16384
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1 + 8192 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 64 * (-128 + x1) + 8192 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-128 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_24(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 128
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + x3, tmp15, None)
    tl.store(out_ptr1 + x3, tmp17, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_25(out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_26(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 17 % 17
    x0 = xindex % 17
    x2 = xindex // 289
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 16, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + x1, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tl.full([XBLOCK], 8, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + x0, tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + 8 * tmp10 + 64 * x2), tmp5 & xmask,
        eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + x4, tmp15, xmask)


@triton.jit
def triton_poi_fused_cat_27(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 256 % 128
    x0 = xindex % 256
    x2 = xindex // 32768
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 256 * x1 + 16384 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 256 * (-64 + x1) + 16384 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-64 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_28(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + x3, tmp15, None)
    tl.store(out_ptr1 + x3, tmp17, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_29(out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_30(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 33 % 33
    x0 = xindex % 33
    x2 = xindex // 1089
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 32, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + x1, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tl.full([XBLOCK], 16, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + x0, tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + 16 * tmp10 + 256 * x2), tmp5 & xmask,
        eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + x4, tmp15, xmask)


@triton.jit
def triton_poi_fused_cat_31(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 1024 % 64
    x0 = xindex % 1024
    x2 = xindex // 65536
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 32768 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 64, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 1024 * (-32 + x1) + 32768 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-32 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_32(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 4096.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + x3, tmp15, None)
    tl.store(out_ptr1 + x3, tmp17, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_33(out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_34(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 540800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 65 % 65
    x0 = xindex % 65
    x2 = xindex // 4225
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 64, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + x1, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tl.full([XBLOCK], 32, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr0 + x0, tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr1 + (tmp14 + 32 * tmp10 + 1024 * x2), tmp5 &
        xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + x4, tmp15, xmask)


@triton.jit
def triton_poi_fused_cat_35(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 32
    x0 = xindex % 4096
    x2 = xindex // 131072
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 32, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-16 + x1) + 65536 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-16 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_36(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52,
        primals_53, primals_54, primals_55, primals_56, primals_57,
        primals_58, primals_59, primals_60, primals_61, primals_62,
        primals_63, primals_64, primals_65, primals_66, primals_67,
        primals_68, primals_69, primals_70, primals_71, primals_72,
        primals_73, primals_74, primals_75, primals_76, primals_77,
        primals_78, primals_79, primals_80, primals_81, primals_82, primals_83
        ) = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (16,), (1,))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (16,), (1,))
    assert_size_stride(primals_10, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_11, (32,), (1,))
    assert_size_stride(primals_12, (32,), (1,))
    assert_size_stride(primals_13, (32,), (1,))
    assert_size_stride(primals_14, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_15, (32,), (1,))
    assert_size_stride(primals_16, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_17, (32,), (1,))
    assert_size_stride(primals_18, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_19, (64,), (1,))
    assert_size_stride(primals_20, (64,), (1,))
    assert_size_stride(primals_21, (64,), (1,))
    assert_size_stride(primals_22, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_23, (64,), (1,))
    assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_25, (64,), (1,))
    assert_size_stride(primals_26, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_27, (128,), (1,))
    assert_size_stride(primals_28, (128,), (1,))
    assert_size_stride(primals_29, (128,), (1,))
    assert_size_stride(primals_30, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_31, (128,), (1,))
    assert_size_stride(primals_32, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_33, (128,), (1,))
    assert_size_stride(primals_34, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_35, (256,), (1,))
    assert_size_stride(primals_36, (256,), (1,))
    assert_size_stride(primals_37, (256,), (1,))
    assert_size_stride(primals_38, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_39, (256,), (1,))
    assert_size_stride(primals_40, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_41, (256,), (1,))
    assert_size_stride(primals_42, (128, 256, 2, 2), (1024, 4, 2, 1))
    assert_size_stride(primals_43, (128,), (1,))
    assert_size_stride(primals_44, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_45, (128,), (1,))
    assert_size_stride(primals_46, (128,), (1,))
    assert_size_stride(primals_47, (128,), (1,))
    assert_size_stride(primals_48, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_49, (128,), (1,))
    assert_size_stride(primals_50, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_51, (128,), (1,))
    assert_size_stride(primals_52, (64, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_53, (64,), (1,))
    assert_size_stride(primals_54, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_55, (64,), (1,))
    assert_size_stride(primals_56, (64,), (1,))
    assert_size_stride(primals_57, (64,), (1,))
    assert_size_stride(primals_58, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_59, (64,), (1,))
    assert_size_stride(primals_60, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_61, (64,), (1,))
    assert_size_stride(primals_62, (32, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_63, (32,), (1,))
    assert_size_stride(primals_64, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_65, (32,), (1,))
    assert_size_stride(primals_66, (32,), (1,))
    assert_size_stride(primals_67, (32,), (1,))
    assert_size_stride(primals_68, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_69, (32,), (1,))
    assert_size_stride(primals_70, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_71, (32,), (1,))
    assert_size_stride(primals_72, (16, 32, 2, 2), (128, 4, 2, 1))
    assert_size_stride(primals_73, (16,), (1,))
    assert_size_stride(primals_74, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_75, (16,), (1,))
    assert_size_stride(primals_76, (16,), (1,))
    assert_size_stride(primals_77, (16,), (1,))
    assert_size_stride(primals_78, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_79, (16,), (1,))
    assert_size_stride(primals_80, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_81, (16,), (1,))
    assert_size_stride(primals_82, (1, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_83, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(262144)](buf1, primals_2, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16),
            torch.float32)
        buf3 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16),
            torch.float32)
        buf4 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16),
            torch.float32)
        triton_red_fused__native_batch_norm_legit_1[grid(32)](buf1, buf2,
            buf3, buf4, 32, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf5 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf8 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        triton_per_fused__native_batch_norm_legit_2[grid(16)](buf2, buf3,
            buf4, buf5, buf6, buf8, 16, 2, XBLOCK=8, num_warps=2, num_stages=1)
        buf9 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_3[grid(262144)](buf1,
            buf5, buf6, primals_4, primals_5, buf9, 262144, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_0[grid(262144)](buf11, primals_7, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf12 = buf4
        del buf4
        buf13 = buf3
        del buf3
        buf14 = buf2
        del buf2
        triton_red_fused__native_batch_norm_legit_1[grid(32)](buf11, buf12,
            buf13, buf14, 32, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf15 = buf6
        del buf6
        buf16 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf18 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_2[grid(16)](buf12, buf13,
            buf14, buf15, buf16, buf18, 16, 2, XBLOCK=8, num_warps=2,
            num_stages=1)
        buf19 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_3[grid(262144)](buf11,
            buf15, buf16, primals_4, primals_5, buf19, 262144, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf20 = extern_kernels.convolution(buf19, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_0[grid(262144)](buf21, primals_9, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_9
        buf22 = buf14
        del buf14
        buf23 = buf13
        del buf13
        buf24 = buf12
        del buf12
        triton_red_fused__native_batch_norm_legit_1[grid(32)](buf21, buf22,
            buf23, buf24, 32, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf25 = buf16
        del buf16
        buf26 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf28 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_2[grid(16)](buf22, buf23,
            buf24, buf25, buf26, buf28, 16, 2, XBLOCK=8, num_warps=2,
            num_stages=1)
        buf29 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_3[grid(262144)](buf21,
            buf25, buf26, primals_4, primals_5, buf29, 262144, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_5
        buf30 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1),
            torch.float32)
        buf31 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_4[grid(65536)](buf29,
            buf30, buf31, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        buf32 = extern_kernels.convolution(buf30, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf33 = buf32
        del buf32
        triton_poi_fused_convolution_5[grid(131072)](buf33, primals_11, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
        buf34 = reinterpret_tensor(buf24, (1, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf24
        buf35 = reinterpret_tensor(buf23, (1, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf23
        buf37 = reinterpret_tensor(buf22, (1, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf22
        triton_red_fused__native_batch_norm_legit_6[grid(32)](buf33, buf34,
            buf35, buf37, 32, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf38 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_7[grid(131072)](buf33,
            buf34, buf35, primals_12, primals_13, buf38, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf39 = extern_kernels.convolution(buf38, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf40 = buf39
        del buf39
        triton_poi_fused_convolution_5[grid(131072)](buf40, primals_15, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_15
        buf41 = buf35
        del buf35
        buf42 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        buf44 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        triton_red_fused__native_batch_norm_legit_6[grid(32)](buf40, buf41,
            buf42, buf44, 32, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf45 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_7[grid(131072)](buf40,
            buf41, buf42, primals_12, primals_13, buf45, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf46 = extern_kernels.convolution(buf45, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf47 = buf46
        del buf46
        triton_poi_fused_convolution_5[grid(131072)](buf47, primals_17, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_17
        buf48 = buf42
        del buf42
        buf49 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        buf51 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        triton_red_fused__native_batch_norm_legit_6[grid(32)](buf47, buf48,
            buf49, buf51, 32, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf52 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_7[grid(131072)](buf47,
            buf48, buf49, primals_12, primals_13, buf52, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_13
        buf53 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1),
            torch.float32)
        buf54 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_8[grid(32768)](buf52,
            buf53, buf54, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        buf55 = extern_kernels.convolution(buf53, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf55, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf56 = buf55
        del buf55
        triton_poi_fused_convolution_9[grid(65536)](buf56, primals_19, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_19
        buf57 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        buf58 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        buf60 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_10[grid(64)](buf56, buf57,
            buf58, buf60, 64, 1024, num_warps=8, num_stages=1)
        buf61 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_11[grid(65536)](buf56,
            buf57, buf58, primals_20, primals_21, buf61, 65536, XBLOCK=512,
            num_warps=4, num_stages=1)
        buf62 = extern_kernels.convolution(buf61, primals_22, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf62, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf63 = buf62
        del buf62
        triton_poi_fused_convolution_9[grid(65536)](buf63, primals_23, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_23
        buf64 = buf58
        del buf58
        buf65 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        buf67 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_10[grid(64)](buf63, buf64,
            buf65, buf67, 64, 1024, num_warps=8, num_stages=1)
        buf68 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_11[grid(65536)](buf63,
            buf64, buf65, primals_20, primals_21, buf68, 65536, XBLOCK=512,
            num_warps=4, num_stages=1)
        buf69 = extern_kernels.convolution(buf68, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf70 = buf69
        del buf69
        triton_poi_fused_convolution_9[grid(65536)](buf70, primals_25, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_25
        buf71 = buf65
        del buf65
        buf72 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        buf74 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_10[grid(64)](buf70, buf71,
            buf72, buf74, 64, 1024, num_warps=8, num_stages=1)
        buf75 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_11[grid(65536)](buf70,
            buf71, buf72, primals_20, primals_21, buf75, 65536, XBLOCK=512,
            num_warps=4, num_stages=1)
        del primals_21
        buf76 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.
            float32)
        buf77 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_12[grid(16384)](buf75,
            buf76, buf77, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        buf78 = extern_kernels.convolution(buf76, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf79 = buf78
        del buf78
        triton_poi_fused_convolution_13[grid(32768)](buf79, primals_27, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_27
        buf80 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf81 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf83 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_14[grid(128)](buf79,
            buf80, buf81, buf83, 128, 256, num_warps=2, num_stages=1)
        buf84 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        triton_poi_fused__native_batch_norm_legit_relu_15[grid(32768)](buf79,
            buf80, buf81, primals_28, primals_29, buf84, 32768, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf85 = extern_kernels.convolution(buf84, primals_30, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf85, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf86 = buf85
        del buf85
        triton_poi_fused_convolution_13[grid(32768)](buf86, primals_31, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_31
        buf87 = buf81
        del buf81
        buf88 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf90 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_14[grid(128)](buf86,
            buf87, buf88, buf90, 128, 256, num_warps=2, num_stages=1)
        buf91 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        triton_poi_fused__native_batch_norm_legit_relu_15[grid(32768)](buf86,
            buf87, buf88, primals_28, primals_29, buf91, 32768, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf92 = extern_kernels.convolution(buf91, primals_32, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf92, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf93 = buf92
        del buf92
        triton_poi_fused_convolution_13[grid(32768)](buf93, primals_33, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_33
        buf94 = buf88
        del buf88
        buf95 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf97 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_14[grid(128)](buf93,
            buf94, buf95, buf97, 128, 256, num_warps=2, num_stages=1)
        buf98 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        triton_poi_fused__native_batch_norm_legit_relu_15[grid(32768)](buf93,
            buf94, buf95, primals_28, primals_29, buf98, 32768, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_29
        buf99 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        buf100 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch
            .int8)
        triton_poi_fused_max_pool2d_with_indices_16[grid(8192)](buf98,
            buf99, buf100, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        buf101 = extern_kernels.convolution(buf99, primals_34, stride=(1, 1
            ), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf102 = buf101
        del buf101
        triton_poi_fused_convolution_17[grid(16384)](buf102, primals_35, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_35
        buf103 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf104 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf106 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_18[grid(256)](buf102,
            buf103, buf104, buf106, 256, 64, XBLOCK=8, num_warps=4,
            num_stages=1)
        buf107 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch
            .float32)
        triton_poi_fused__native_batch_norm_legit_relu_19[grid(16384)](buf102,
            buf103, buf104, primals_36, primals_37, buf107, 16384, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf108 = extern_kernels.convolution(buf107, primals_38, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf109 = buf108
        del buf108
        triton_poi_fused_convolution_17[grid(16384)](buf109, primals_39, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_39
        buf110 = buf104
        del buf104
        buf111 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf113 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_18[grid(256)](buf109,
            buf110, buf111, buf113, 256, 64, XBLOCK=8, num_warps=4,
            num_stages=1)
        buf114 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch
            .float32)
        triton_poi_fused__native_batch_norm_legit_relu_19[grid(16384)](buf109,
            buf110, buf111, primals_36, primals_37, buf114, 16384, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf115 = extern_kernels.convolution(buf114, primals_40, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf115, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf116 = buf115
        del buf115
        triton_poi_fused_convolution_17[grid(16384)](buf116, primals_41, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_41
        buf117 = buf111
        del buf111
        buf118 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf120 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_18[grid(256)](buf116,
            buf117, buf118, buf120, 256, 64, XBLOCK=8, num_warps=4,
            num_stages=1)
        buf121 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch
            .float32)
        buf236 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch
            .bool)
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_20[
            grid(16384)](buf116, buf117, buf118, primals_36, primals_37,
            buf121, buf236, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del buf118
        del primals_37
        buf122 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_21[grid(8)](buf122, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        buf123 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_constant_pad_nd_22[grid(82944)](buf122,
            buf121, buf123, 82944, XBLOCK=512, num_warps=8, num_stages=1)
        del buf121
        buf124 = extern_kernels.convolution(buf123, primals_42, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf124, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf125 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1),
            torch.float32)
        triton_poi_fused_cat_23[grid(65536)](buf98, buf124, primals_43,
            buf125, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_43
        buf126 = extern_kernels.convolution(buf125, primals_44, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf126, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf127 = buf126
        del buf126
        triton_poi_fused_convolution_13[grid(32768)](buf127, primals_45, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_45
        buf128 = buf95
        del buf95
        buf129 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf131 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_14[grid(128)](buf127,
            buf128, buf129, buf131, 128, 256, num_warps=2, num_stages=1)
        buf132 = buf124
        del buf124
        triton_poi_fused__native_batch_norm_legit_relu_15[grid(32768)](buf127,
            buf128, buf129, primals_46, primals_47, buf132, 32768, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf133 = extern_kernels.convolution(buf132, primals_48, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf133, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf134 = buf133
        del buf133
        triton_poi_fused_convolution_13[grid(32768)](buf134, primals_49, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_49
        buf135 = buf129
        del buf129
        buf136 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf138 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_14[grid(128)](buf134,
            buf135, buf136, buf138, 128, 256, num_warps=2, num_stages=1)
        buf139 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch
            .float32)
        triton_poi_fused__native_batch_norm_legit_relu_15[grid(32768)](buf134,
            buf135, buf136, primals_46, primals_47, buf139, 32768, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf140 = extern_kernels.convolution(buf139, primals_50, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf140, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf141 = buf140
        del buf140
        triton_poi_fused_convolution_13[grid(32768)](buf141, primals_51, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_51
        buf142 = buf136
        del buf136
        buf143 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf145 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_14[grid(128)](buf141,
            buf142, buf143, buf145, 128, 256, num_warps=2, num_stages=1)
        buf146 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch
            .float32)
        buf235 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch
            .bool)
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_24[
            grid(32768)](buf141, buf142, buf143, primals_46, primals_47,
            buf146, buf235, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del buf143
        del primals_47
        buf147 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_25[grid(16)](buf147, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf148 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_constant_pad_nd_26[grid(147968)](buf147,
            buf146, buf148, 147968, XBLOCK=512, num_warps=8, num_stages=1)
        del buf146
        buf149 = extern_kernels.convolution(buf148, primals_52, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf149, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf150 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_poi_fused_cat_27[grid(131072)](buf75, buf149, primals_53,
            buf150, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_53
        buf151 = extern_kernels.convolution(buf150, primals_54, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf151, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf152 = buf151
        del buf151
        triton_poi_fused_convolution_9[grid(65536)](buf152, primals_55, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_55
        buf153 = buf72
        del buf72
        buf154 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        buf156 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_10[grid(64)](buf152,
            buf153, buf154, buf156, 64, 1024, num_warps=8, num_stages=1)
        buf157 = buf149
        del buf149
        triton_poi_fused__native_batch_norm_legit_relu_11[grid(65536)](buf152,
            buf153, buf154, primals_56, primals_57, buf157, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        buf158 = extern_kernels.convolution(buf157, primals_58, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf158, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf159 = buf158
        del buf158
        triton_poi_fused_convolution_9[grid(65536)](buf159, primals_59, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_59
        buf160 = buf154
        del buf154
        buf161 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        buf163 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_10[grid(64)](buf159,
            buf160, buf161, buf163, 64, 1024, num_warps=8, num_stages=1)
        buf164 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_11[grid(65536)](buf159,
            buf160, buf161, primals_56, primals_57, buf164, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        buf165 = extern_kernels.convolution(buf164, primals_60, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf165, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf166 = buf165
        del buf165
        triton_poi_fused_convolution_9[grid(65536)](buf166, primals_61, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_61
        buf167 = buf161
        del buf161
        buf168 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        buf170 = empty_strided_cuda((1, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_10[grid(64)](buf166,
            buf167, buf168, buf170, 64, 1024, num_warps=8, num_stages=1)
        buf171 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.float32)
        buf234 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.bool)
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_28[
            grid(65536)](buf166, buf167, buf168, primals_56, primals_57,
            buf171, buf234, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del buf168
        del primals_57
        buf172 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_29[grid(32)](buf172, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf173 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_constant_pad_nd_30[grid(278784)](buf172,
            buf171, buf173, 278784, XBLOCK=512, num_warps=8, num_stages=1)
        del buf171
        buf174 = extern_kernels.convolution(buf173, primals_62, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf174, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf175 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_cat_31[grid(262144)](buf52, buf174, primals_63,
            buf175, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_63
        buf176 = extern_kernels.convolution(buf175, primals_64, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf176, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf177 = buf176
        del buf176
        triton_poi_fused_convolution_5[grid(131072)](buf177, primals_65, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_65
        buf178 = buf49
        del buf49
        buf179 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        buf181 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        triton_red_fused__native_batch_norm_legit_6[grid(32)](buf177,
            buf178, buf179, buf181, 32, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf182 = buf174
        del buf174
        triton_poi_fused__native_batch_norm_legit_relu_7[grid(131072)](buf177,
            buf178, buf179, primals_66, primals_67, buf182, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        buf183 = extern_kernels.convolution(buf182, primals_68, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf183, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf184 = buf183
        del buf183
        triton_poi_fused_convolution_5[grid(131072)](buf184, primals_69, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_69
        buf185 = buf179
        del buf179
        buf186 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        buf188 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        triton_red_fused__native_batch_norm_legit_6[grid(32)](buf184,
            buf185, buf186, buf188, 32, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf189 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_7[grid(131072)](buf184,
            buf185, buf186, primals_66, primals_67, buf189, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        buf190 = extern_kernels.convolution(buf189, primals_70, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf190, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf191 = buf190
        del buf190
        triton_poi_fused_convolution_5[grid(131072)](buf191, primals_71, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_71
        buf192 = buf186
        del buf186
        buf193 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        buf195 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.
            float32)
        triton_red_fused__native_batch_norm_legit_6[grid(32)](buf191,
            buf192, buf193, buf195, 32, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf196 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.float32)
        buf233 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.bool)
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_32[
            grid(131072)](buf191, buf192, buf193, primals_66, primals_67,
            buf196, buf233, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_67
        buf197 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_33[grid(64)](buf197, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf198 = empty_strided_cuda((4, 32, 65, 65), (135200, 4225, 65, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_constant_pad_nd_34[grid(540800)](buf197,
            buf196, buf198, 540800, XBLOCK=512, num_warps=8, num_stages=1)
        del buf196
        buf199 = extern_kernels.convolution(buf198, primals_72, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf199, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf200 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_35[grid(524288)](buf29, buf199, primals_73,
            buf200, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_73
        buf201 = extern_kernels.convolution(buf200, primals_74, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf201, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf202 = buf201
        del buf201
        triton_poi_fused_convolution_0[grid(262144)](buf202, primals_75, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_75
        buf203 = reinterpret_tensor(buf193, (1, 16, 1, 1, 2), (32, 1, 32, 
            32, 16), 0)
        del buf193
        buf204 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16),
            torch.float32)
        buf205 = empty_strided_cuda((1, 16, 1, 1, 2), (32, 1, 32, 32, 16),
            torch.float32)
        triton_red_fused__native_batch_norm_legit_1[grid(32)](buf202,
            buf203, buf204, buf205, 32, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf206 = buf26
        del buf26
        buf207 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf209 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_2[grid(16)](buf203,
            buf204, buf205, buf206, buf207, buf209, 16, 2, XBLOCK=8,
            num_warps=2, num_stages=1)
        buf210 = buf199
        del buf199
        triton_poi_fused__native_batch_norm_legit_relu_3[grid(262144)](buf202,
            buf206, buf207, primals_76, primals_77, buf210, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        buf211 = extern_kernels.convolution(buf210, primals_78, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf211, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf212 = buf211
        del buf211
        triton_poi_fused_convolution_0[grid(262144)](buf212, primals_79, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_79
        buf213 = buf205
        del buf205
        buf214 = buf204
        del buf204
        buf215 = buf203
        del buf203
        triton_red_fused__native_batch_norm_legit_1[grid(32)](buf212,
            buf213, buf214, buf215, 32, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf216 = buf207
        del buf207
        buf217 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf219 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_2[grid(16)](buf213,
            buf214, buf215, buf216, buf217, buf219, 16, 2, XBLOCK=8,
            num_warps=2, num_stages=1)
        buf220 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_3[grid(262144)](buf212,
            buf216, buf217, primals_76, primals_77, buf220, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        buf221 = extern_kernels.convolution(buf220, primals_80, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf221, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf222 = buf221
        del buf221
        triton_poi_fused_convolution_0[grid(262144)](buf222, primals_81, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_81
        buf223 = buf215
        del buf215
        buf224 = buf214
        del buf214
        buf225 = buf213
        del buf213
        triton_red_fused__native_batch_norm_legit_1[grid(32)](buf222,
            buf223, buf224, buf225, 32, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf226 = buf217
        del buf217
        buf227 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf229 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_2[grid(16)](buf223,
            buf224, buf225, buf226, buf227, buf229, 16, 2, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf223
        del buf224
        del buf225
        buf230 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_relu_3[grid(262144)](buf222,
            buf226, buf227, primals_76, primals_77, buf230, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del buf227
        del primals_77
        buf231 = extern_kernels.convolution(buf230, primals_82, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf231, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf232 = buf231
        del buf231
        triton_poi_fused_convolution_36[grid(16384)](buf232, primals_83, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_83
    return (buf232, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, primals_18,
        primals_20, primals_22, primals_24, primals_26, primals_28,
        primals_30, primals_32, primals_34, primals_36, primals_38,
        primals_40, primals_42, primals_44, primals_46, primals_48,
        primals_50, primals_52, primals_54, primals_56, primals_58,
        primals_60, primals_62, primals_64, primals_66, primals_68,
        primals_70, primals_72, primals_74, primals_76, primals_78,
        primals_80, primals_82, buf1, reinterpret_tensor(buf8, (16,), (1,),
        0), buf9, buf11, reinterpret_tensor(buf18, (16,), (1,), 0), buf19,
        buf21, reinterpret_tensor(buf28, (16,), (1,), 0), buf29, buf30,
        buf31, buf33, reinterpret_tensor(buf37, (32,), (1,), 0), buf38,
        buf40, reinterpret_tensor(buf44, (32,), (1,), 0), buf45, buf47,
        reinterpret_tensor(buf51, (32,), (1,), 0), buf52, buf53, buf54,
        buf56, reinterpret_tensor(buf60, (64,), (1,), 0), buf61, buf63,
        reinterpret_tensor(buf67, (64,), (1,), 0), buf68, buf70,
        reinterpret_tensor(buf74, (64,), (1,), 0), buf75, buf76, buf77,
        buf79, reinterpret_tensor(buf83, (128,), (1,), 0), buf84, buf86,
        reinterpret_tensor(buf90, (128,), (1,), 0), buf91, buf93,
        reinterpret_tensor(buf97, (128,), (1,), 0), buf98, buf99, buf100,
        buf102, reinterpret_tensor(buf106, (256,), (1,), 0), buf107, buf109,
        reinterpret_tensor(buf113, (256,), (1,), 0), buf114, buf116,
        reinterpret_tensor(buf120, (256,), (1,), 0), buf122, buf123, buf125,
        buf127, reinterpret_tensor(buf131, (128,), (1,), 0), buf132, buf134,
        reinterpret_tensor(buf138, (128,), (1,), 0), buf139, buf141,
        reinterpret_tensor(buf145, (128,), (1,), 0), buf147, buf148, buf150,
        buf152, reinterpret_tensor(buf156, (64,), (1,), 0), buf157, buf159,
        reinterpret_tensor(buf163, (64,), (1,), 0), buf164, buf166,
        reinterpret_tensor(buf170, (64,), (1,), 0), buf172, buf173, buf175,
        buf177, reinterpret_tensor(buf181, (32,), (1,), 0), buf182, buf184,
        reinterpret_tensor(buf188, (32,), (1,), 0), buf189, buf191,
        reinterpret_tensor(buf195, (32,), (1,), 0), buf197, buf198, buf200,
        buf202, reinterpret_tensor(buf209, (16,), (1,), 0), buf210, buf212,
        reinterpret_tensor(buf219, (16,), (1,), 0), buf220, buf222,
        reinterpret_tensor(buf229, (16,), (1,), 0), buf230,
        reinterpret_tensor(buf226, (1, 16, 1, 1), (16, 1, 1, 1), 0),
        reinterpret_tensor(buf216, (1, 16, 1, 1), (16, 1, 1, 1), 0),
        reinterpret_tensor(buf206, (1, 16, 1, 1), (16, 1, 1, 1), 0), buf233,
        reinterpret_tensor(buf192, (1, 32, 1, 1), (32, 1, 1, 1), 0),
        reinterpret_tensor(buf185, (1, 32, 1, 1), (32, 1, 1, 1), 0),
        reinterpret_tensor(buf178, (1, 32, 1, 1), (32, 1, 1, 1), 0), buf234,
        reinterpret_tensor(buf167, (1, 64, 1, 1), (64, 1, 1, 1), 0),
        reinterpret_tensor(buf160, (1, 64, 1, 1), (64, 1, 1, 1), 0),
        reinterpret_tensor(buf153, (1, 64, 1, 1), (64, 1, 1, 1), 0), buf235,
        reinterpret_tensor(buf142, (1, 128, 1, 1), (128, 1, 1, 1), 0),
        reinterpret_tensor(buf135, (1, 128, 1, 1), (128, 1, 1, 1), 0),
        reinterpret_tensor(buf128, (1, 128, 1, 1), (128, 1, 1, 1), 0),
        buf236, reinterpret_tensor(buf117, (1, 256, 1, 1), (256, 1, 1, 1), 
        0), reinterpret_tensor(buf110, (1, 256, 1, 1), (256, 1, 1, 1), 0),
        reinterpret_tensor(buf103, (1, 256, 1, 1), (256, 1, 1, 1), 0),
        reinterpret_tensor(buf94, (1, 128, 1, 1), (128, 1, 1, 1), 0),
        reinterpret_tensor(buf87, (1, 128, 1, 1), (128, 1, 1, 1), 0),
        reinterpret_tensor(buf80, (1, 128, 1, 1), (128, 1, 1, 1), 0),
        reinterpret_tensor(buf71, (1, 64, 1, 1), (64, 1, 1, 1), 0),
        reinterpret_tensor(buf64, (1, 64, 1, 1), (64, 1, 1, 1), 0),
        reinterpret_tensor(buf57, (1, 64, 1, 1), (64, 1, 1, 1), 0),
        reinterpret_tensor(buf48, (1, 32, 1, 1), (32, 1, 1, 1), 0),
        reinterpret_tensor(buf41, (1, 32, 1, 1), (32, 1, 1, 1), 0),
        reinterpret_tensor(buf34, (1, 32, 1, 1), (32, 1, 1, 1), 0),
        reinterpret_tensor(buf25, (1, 16, 1, 1), (16, 1, 1, 1), 0),
        reinterpret_tensor(buf15, (1, 16, 1, 1), (16, 1, 1, 1), 0),
        reinterpret_tensor(buf5, (1, 16, 1, 1), (16, 1, 1, 1), 0))


def conv3x3(in_channels, out_channels):
    return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, bias=True)


def maxpool2x2():
    return nn.MaxPool2d(kernel_size=2, stride=2, padding=0)


def concat(xh, xv):
    return torch.cat([xh, xv], dim=1)


class UpConv2x2(nn.Module):

    def __init__(self, channels):
        super(UpConv2x2, self).__init__()
        self.conv = nn.Conv2d(channels, channels // 2, kernel_size=2,
            stride=1, padding=0, bias=True)

    def forward(self, x):
        x = F.interpolate(x, scale_factor=2, mode='nearest')
        x = F.pad(x, (0, 1, 0, 1))
        x = self.conv(x)
        return x


class ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(ConvBlock, self).__init__()
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

    def forward(self, x):
        x = F.relu(self.norm(self.conv1(x)))
        x = F.relu(self.norm(self.conv2(x)))
        x = F.relu(self.norm(self.conv3(x)))
        return x


class DownConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(DownConvBlock, self).__init__()
        self.maxpool = maxpool2x2()
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

    def forward(self, x):
        x = self.maxpool(x)
        x = F.relu(self.norm(self.conv1(x)))
        x = F.relu(self.norm(self.conv2(x)))
        x = F.relu(self.norm(self.conv3(x)))
        return x


class UpConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels):
        """
        Args:
            in_channels: number of channels in input (1st) feature map
            out_channels: number of channels in output feature maps
        """
        super(UpConvBlock, self).__init__()
        self.upconv = UpConv2x2(in_channels)
        self.conv1 = conv3x3(in_channels, out_channels)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.conv3 = conv3x3(out_channels, out_channels)
        self.norm = nn.BatchNorm2d(out_channels, track_running_stats=False)

    def forward(self, xh, xv):
        """
        Args:
            xh: torch Variable, activations from same resolution feature maps (gray arrow in diagram)
            xv: torch Variable, activations from lower resolution feature maps (green arrow in diagram)
        """
        xv = self.upconv(xv)
        x = concat(xh, xv)
        x = F.relu(self.norm(self.conv1(x)))
        x = F.relu(self.norm(self.conv2(x)))
        x = F.relu(self.norm(self.conv3(x)))
        return x


class UNetNew(nn.Module):

    def __init__(self):
        super(UNetNew, self).__init__()
        fs = [16, 32, 64, 128, 256]
        self.conv_in = ConvBlock(1, fs[0])
        self.dconv1 = DownConvBlock(fs[0], fs[1])
        self.dconv2 = DownConvBlock(fs[1], fs[2])
        self.dconv3 = DownConvBlock(fs[2], fs[3])
        self.dconv4 = DownConvBlock(fs[3], fs[4])
        self.uconv1 = UpConvBlock(fs[4], fs[3])
        self.uconv2 = UpConvBlock(fs[3], fs[2])
        self.uconv3 = UpConvBlock(fs[2], fs[1])
        self.uconv4 = UpConvBlock(fs[1], fs[0])
        self.conv_out = conv3x3(fs[0], 1)
        self._initialize_weights()

    def _initialize_weights(self):
        conv_modules = [m for m in self.modules() if isinstance(m, nn.Conv2d)]
        for m in conv_modules:
            n = m.weight.shape[1] * m.weight.shape[2] * m.weight.shape[3]
            m.weight.data.normal_(0, np.sqrt(2.0 / n))
            m.bias.data.zero_()

    def forward(self, input_0):
        primals_1 = self.conv_in.conv1.weight
        primals_2 = self.conv_in.conv1.bias
        primals_6 = self.conv_in.conv2.weight
        primals_4 = self.conv_in.conv2.bias
        primals_8 = self.conv_in.conv3.weight
        primals_5 = self.conv_in.conv3.bias
        primals_7 = self.conv_in.norm.weight
        primals_9 = self.conv_in.norm.bias
        primals_10 = self.dconv1.conv1.weight
        primals_11 = self.dconv1.conv1.bias
        primals_14 = self.dconv1.conv2.weight
        primals_12 = self.dconv1.conv2.bias
        primals_16 = self.dconv1.conv3.weight
        primals_13 = self.dconv1.conv3.bias
        primals_15 = self.dconv1.norm.weight
        primals_17 = self.dconv1.norm.bias
        primals_18 = self.dconv2.conv1.weight
        primals_19 = self.dconv2.conv1.bias
        primals_22 = self.dconv2.conv2.weight
        primals_20 = self.dconv2.conv2.bias
        primals_24 = self.dconv2.conv3.weight
        primals_21 = self.dconv2.conv3.bias
        primals_23 = self.dconv2.norm.weight
        primals_25 = self.dconv2.norm.bias
        primals_26 = self.dconv3.conv1.weight
        primals_27 = self.dconv3.conv1.bias
        primals_30 = self.dconv3.conv2.weight
        primals_28 = self.dconv3.conv2.bias
        primals_32 = self.dconv3.conv3.weight
        primals_29 = self.dconv3.conv3.bias
        primals_31 = self.dconv3.norm.weight
        primals_33 = self.dconv3.norm.bias
        primals_34 = self.dconv4.conv1.weight
        primals_35 = self.dconv4.conv1.bias
        primals_38 = self.dconv4.conv2.weight
        primals_36 = self.dconv4.conv2.bias
        primals_40 = self.dconv4.conv3.weight
        primals_37 = self.dconv4.conv3.bias
        primals_39 = self.dconv4.norm.weight
        primals_41 = self.dconv4.norm.bias
        primals_42 = self.uconv1.upconv.conv.weight
        primals_43 = self.uconv1.upconv.conv.bias
        primals_44 = self.uconv1.conv1.weight
        primals_45 = self.uconv1.conv1.bias
        primals_48 = self.uconv1.conv2.weight
        primals_46 = self.uconv1.conv2.bias
        primals_50 = self.uconv1.conv3.weight
        primals_47 = self.uconv1.conv3.bias
        primals_49 = self.uconv1.norm.weight
        primals_51 = self.uconv1.norm.bias
        primals_52 = self.uconv2.upconv.conv.weight
        primals_53 = self.uconv2.upconv.conv.bias
        primals_54 = self.uconv2.conv1.weight
        primals_55 = self.uconv2.conv1.bias
        primals_58 = self.uconv2.conv2.weight
        primals_56 = self.uconv2.conv2.bias
        primals_60 = self.uconv2.conv3.weight
        primals_57 = self.uconv2.conv3.bias
        primals_59 = self.uconv2.norm.weight
        primals_61 = self.uconv2.norm.bias
        primals_62 = self.uconv3.upconv.conv.weight
        primals_63 = self.uconv3.upconv.conv.bias
        primals_64 = self.uconv3.conv1.weight
        primals_65 = self.uconv3.conv1.bias
        primals_68 = self.uconv3.conv2.weight
        primals_66 = self.uconv3.conv2.bias
        primals_70 = self.uconv3.conv3.weight
        primals_67 = self.uconv3.conv3.bias
        primals_69 = self.uconv3.norm.weight
        primals_71 = self.uconv3.norm.bias
        primals_72 = self.uconv4.upconv.conv.weight
        primals_73 = self.uconv4.upconv.conv.bias
        primals_74 = self.uconv4.conv1.weight
        primals_75 = self.uconv4.conv1.bias
        primals_78 = self.uconv4.conv2.weight
        primals_76 = self.uconv4.conv2.bias
        primals_80 = self.uconv4.conv3.weight
        primals_77 = self.uconv4.conv3.bias
        primals_79 = self.uconv4.norm.weight
        primals_81 = self.uconv4.norm.bias
        primals_82 = self.conv_out.weight
        primals_83 = self.conv_out.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53, primals_54,
            primals_55, primals_56, primals_57, primals_58, primals_59,
            primals_60, primals_61, primals_62, primals_63, primals_64,
            primals_65, primals_66, primals_67, primals_68, primals_69,
            primals_70, primals_71, primals_72, primals_73, primals_74,
            primals_75, primals_76, primals_77, primals_78, primals_79,
            primals_80, primals_81, primals_82, primals_83])
        return output[0]
