# 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_7/inductor_cache/xf/cxfcqgqzkxpafh7ngi5lmyvtgswx4bwrz3upkkjlnp6wzixzpn5k.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.01), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_0', '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_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/av/cavhjnlb3jhzdiam6ww4odoew4b7etx4mfdwllv5macorejth2wz.py
# Topologically Sorted Source Nodes: [x_2, iadd, x_3], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd => add
#   x_2 => convolution_1
#   x_3 => gt_1, mul_1, where_1
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_2, %primals_1), kwargs = {})
#   %slice_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor, %add, 3, 0, 9223372036854775807), kwargs = {})
#   %slice_scatter_default_1 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%convolution_1, %slice_scatter_default, 1, 0, 1), kwargs = {})
#   %slice_scatter_default_2 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_1, %slice_12, 1, 0, 1), kwargs = {})
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%slice_scatter_default_2, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_scatter_default_2, 0.01), kwargs = {})
#   %where_1 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %slice_scatter_default_2, %mul_1), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_1 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_convolution_leaky_relu_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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
    x3 = xindex
    x0 = xindex % 4096
    x2 = (xindex // 131072)
    tmp21 = tl.load(in_ptr0 + (x3), None)
    tmp22 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + (x3), tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + (x1), tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x0 + (4096*x2)), tmp3, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + (x3), tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x1), tmp2, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x0 + (4096*x2)), tmp2, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + (x3), tmp27, None)
    tl.store(out_ptr1 + (x3), tmp30, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ji/cji5l2esho4uvrao5eeomydgbe3hjk4sdot5hvrdbfwiux6vsdse.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_4 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_6, %primals_7, [2, 2], [0, 0], [1, 1], False, [0, 0], 32), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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.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_2', '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_2(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_7/inductor_cache/42/c42ryamg2fbchnrdanzxiam3yphdwkdejaxnqprizpnvuejwqtar.py
# Topologically Sorted Source Nodes: [x_5, x_6], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_5 => convolution_3
#   x_6 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_3 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_2, %primals_8, %primals_9, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 0.01), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_3, %mul_2), kwargs = {})
triton_poi_fused_convolution_leaky_relu_3 = async_compile.triton('triton_poi_fused_convolution_leaky_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: '*i1', 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_convolution_leaky_relu_3', '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_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 // 1024) % 64
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ag/cageczbpbdq2zvqdke7tpgave3qsnz6g7oqwi32lwbqxmfh6cqgr.py
# Topologically Sorted Source Nodes: [x_7, iadd_1, x_8], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_1 => add_1
#   x_7 => convolution_4
#   x_8 => gt_3, mul_3, where_3
# Graph fragment:
#   %convolution_4 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_39, %convolution_2), kwargs = {})
#   %slice_scatter_default_3 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor_1, %add_1, 3, 0, 9223372036854775807), kwargs = {})
#   %slice_scatter_default_4 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%convolution_4, %slice_scatter_default_3, 1, 0, 32), kwargs = {})
#   %slice_scatter_default_5 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_4, %slice_49, 1, 0, 32), kwargs = {})
#   %gt_3 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%slice_scatter_default_5, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_scatter_default_5, 0.01), kwargs = {})
#   %where_3 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %slice_scatter_default_5, %mul_3), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_4 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_convolution_leaky_relu_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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
    x3 = xindex
    x2 = (xindex // 65536)
    x4 = xindex % 65536
    tmp21 = tl.load(in_ptr0 + (x3), None)
    tmp22 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 32, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + (x3), tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + (x1), tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x4 + (32768*x2)), tmp3, other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + (x3), tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x1), tmp2, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x4 + (32768*x2)), tmp2, other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + (x3), tmp27, None)
    tl.store(out_ptr1 + (x3), tmp30, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wm/cwmx573hgzpqv72fn6et3riktwpbit3or4nztj7ce6dfrczkaddw.py
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_9 => convolution_5
# Graph fragment:
#   %convolution_5 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_3, %primals_12, %primals_13, [2, 2], [0, 0], [1, 1], False, [0, 0], 64), 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=[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_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 = 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_7/inductor_cache/3w/c3wcxzpxikyyqdrw6kpbwbywojsljwovzvu54xks2iemn2a2bek6.py
# Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_10 => convolution_6
#   x_11 => gt_4, mul_4, where_4
# Graph fragment:
#   %convolution_6 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_5, %primals_14, %primals_15, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_4 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_6, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_6, 0.01), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %convolution_6, %mul_4), kwargs = {})
triton_poi_fused_convolution_leaky_relu_6 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_6', '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_convolution_leaky_relu_6(in_ptr0, in_ptr1, 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 // 256) % 128
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uq/cuqzrsdrmc6acrieikbfirzninntl7f3rpxs5iatziafgkhripbr.py
# Topologically Sorted Source Nodes: [x_12, iadd_2, x_13], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_2 => add_2
#   x_12 => convolution_7
#   x_13 => gt_5, mul_5, where_5
# Graph fragment:
#   %convolution_7 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_4, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_76, %convolution_5), kwargs = {})
#   %slice_scatter_default_6 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor_2, %add_2, 3, 0, 9223372036854775807), kwargs = {})
#   %slice_scatter_default_7 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%convolution_7, %slice_scatter_default_6, 1, 0, 64), kwargs = {})
#   %slice_scatter_default_8 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_7, %slice_86, 1, 0, 64), kwargs = {})
#   %gt_5 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%slice_scatter_default_8, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_scatter_default_8, 0.01), kwargs = {})
#   %where_5 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %slice_scatter_default_8, %mul_5), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_7 = async_compile.triton('triton_poi_fused_add_convolution_leaky_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: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_convolution_leaky_relu_7(in_ptr0, in_ptr1, in_ptr2, 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)
    x1 = (xindex // 256) % 128
    x3 = xindex
    x2 = (xindex // 32768)
    x4 = xindex % 32768
    tmp21 = tl.load(in_ptr0 + (x3), None)
    tmp22 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 64, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + (x3), tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + (x1), tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x4 + (16384*x2)), tmp3, other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + (x3), tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x1), tmp2, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x4 + (16384*x2)), tmp2, other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + (x3), tmp27, None)
    tl.store(out_ptr1 + (x3), tmp30, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/z5/cz5htevaalapzdiv2ghjv26w2a3mj4iej6i77fm4swdcujsblszp.py
# Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_14 => convolution_8
# Graph fragment:
#   %convolution_8 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_5, %primals_18, %primals_19, [2, 2], [0, 0], [1, 1], False, [0, 0], 128), kwargs = {})
triton_poi_fused_convolution_8 = async_compile.triton('triton_poi_fused_convolution_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: '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_8', '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_8(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_7/inductor_cache/qm/cqmscszyypzxc4zzgkthiigoojenozafjwojwc52fv6pbxgqrvep.py
# Topologically Sorted Source Nodes: [x_15, x_16], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_15 => convolution_9
#   x_16 => gt_6, mul_6, where_6
# Graph fragment:
#   %convolution_9 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_8, %primals_20, %primals_21, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_6 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_9, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_9, 0.01), kwargs = {})
#   %where_6 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %convolution_9, %mul_6), kwargs = {})
triton_poi_fused_convolution_leaky_relu_9 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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: '*i1', 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_convolution_leaky_relu_9', '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_convolution_leaky_relu_9(in_ptr0, in_ptr1, 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 // 64) % 256
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/pn/cpnmjt3x2nmyv3a3tzn6a3wszlt7xdk4x5wv6ffqsn324kfaib4f.py
# Topologically Sorted Source Nodes: [x_17, iadd_3, x_18], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_3 => add_3
#   x_17 => convolution_10
#   x_18 => gt_7, mul_7, where_7
# Graph fragment:
#   %convolution_10 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_6, %primals_22, %primals_23, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_113, %convolution_8), kwargs = {})
#   %slice_scatter_default_9 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor_3, %add_3, 3, 0, 9223372036854775807), kwargs = {})
#   %slice_scatter_default_10 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%convolution_10, %slice_scatter_default_9, 1, 0, 128), kwargs = {})
#   %slice_scatter_default_11 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_10, %slice_123, 1, 0, 128), kwargs = {})
#   %gt_7 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%slice_scatter_default_11, 0), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_scatter_default_11, 0.01), kwargs = {})
#   %where_7 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_7, %slice_scatter_default_11, %mul_7), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_10 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_convolution_leaky_relu_10(in_ptr0, in_ptr1, in_ptr2, 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)
    x1 = (xindex // 64) % 256
    x3 = xindex
    x2 = (xindex // 16384)
    x4 = xindex % 16384
    tmp21 = tl.load(in_ptr0 + (x3), None)
    tmp22 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 128, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + (x3), tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + (x1), tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x4 + (8192*x2)), tmp3, other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + (x3), tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x1), tmp2, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x4 + (8192*x2)), tmp2, other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + (x3), tmp27, None)
    tl.store(out_ptr1 + (x3), tmp30, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/pn/cpntx5ingplfviuf3scpf2ploxf2cbj6dppd5twxxapjxxzeb2gw.py
# Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_19 => convolution_11
# Graph fragment:
#   %convolution_11 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_7, %primals_24, %primals_25, [2, 2], [0, 0], [1, 1], False, [0, 0], 256), kwargs = {})
triton_poi_fused_convolution_11 = async_compile.triton('triton_poi_fused_convolution_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=[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_11', '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_11(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_7/inductor_cache/5p/c5pvu73hvxjushlgcdbeq425vx4apnqtyg3qns5wrorg2nbmszzc.py
# Topologically Sorted Source Nodes: [x_20, x_21], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_20 => convolution_12
#   x_21 => gt_8, mul_8, where_8
# Graph fragment:
#   %convolution_12 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_11, %primals_26, %primals_27, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_8 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_12, 0), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_12, 0.01), kwargs = {})
#   %where_8 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_8, %convolution_12, %mul_8), kwargs = {})
triton_poi_fused_convolution_leaky_relu_12 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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: '*i1', 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_convolution_leaky_relu_12', '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_convolution_leaky_relu_12(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rh/crhinxcz2vvog2xvydnfton7wspkzsg5t3cjjz2xt2wuzhoi354w.py
# Topologically Sorted Source Nodes: [x_22, iadd_4, x_23], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_4 => add_4
#   x_22 => convolution_13
#   x_23 => gt_9, mul_9, where_9
# Graph fragment:
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_8, %primals_28, %primals_29, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_4 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_13, %convolution_11), kwargs = {})
#   %gt_9 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_4, 0), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_4, 0.01), kwargs = {})
#   %where_9 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_9, %add_4, %mul_9), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_13 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_13', '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_add_convolution_leaky_relu_13(in_ptr0, in_ptr1, in_ptr2, 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 + (x3), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xz/cxzdy4wlhiutyuwqhvz5mul3hsest2j4rhjcdy5iicga6ukzem6f.py
# Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_25 => cat
# Graph fragment:
#   %cat : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_14, %where_7], 1), kwargs = {})
triton_poi_fused_cat_14 = async_compile.triton('triton_poi_fused_cat_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.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_14', '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_14(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 // 64) % 512
    x0 = xindex % 64
    x2 = (xindex // 32768)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (64*x1) + (16384*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 512, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (64*((-256) + x1)) + (16384*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zt/cztj2a2rppkblw2uusvgftlbe5t6xd2fvbauq357vhaoyj3caget.py
# Topologically Sorted Source Nodes: [x_26, x_27], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_26 => convolution_15
#   x_27 => gt_10, mul_10, where_10
# Graph fragment:
#   %convolution_15 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_32, %primals_33, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_10 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_15, 0), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_15, 0.01), kwargs = {})
#   %where_10 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_10, %convolution_15, %mul_10), kwargs = {})
triton_poi_fused_convolution_leaky_relu_15 = async_compile.triton('triton_poi_fused_convolution_leaky_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: '*i1', 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_convolution_leaky_relu_15', '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_convolution_leaky_relu_15(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/eq/ceqlawpx2k7y4mlllr6mlufqinhrt5n2lyebv6w73yy753ifmwl4.py
# Topologically Sorted Source Nodes: [x_28, iadd_5, x_29], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_5 => add_5
#   x_28 => convolution_16
#   x_29 => gt_11, mul_11, where_11
# Graph fragment:
#   %convolution_16 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_10, %primals_34, %primals_35, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_5 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_16, %slice_181), kwargs = {})
#   %gt_11 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_5, 0), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.01), kwargs = {})
#   %where_11 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_11, %add_5, %mul_11), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_16 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_16', '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_add_convolution_leaky_relu_16(in_ptr0, in_ptr1, in_ptr2, 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
    x2 = (xindex // 8192)
    x4 = xindex % 8192
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + (32768*x2)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/si/csi3nwzyblsdh2cv7kre43tug6gju4orzk6cf7c7tldsjdjh4fge.py
# Topologically Sorted Source Nodes: [x_31], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_31 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_17, %where_5], 1), kwargs = {})
triton_poi_fused_cat_17 = async_compile.triton('triton_poi_fused_cat_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=[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_17', '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_17(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 // 256) % 256
    x0 = xindex % 256
    x2 = (xindex // 65536)
    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 + (256*x1) + (32768*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 256, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (256*((-128) + x1)) + (32768*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vg/cvgp54mfma7r4duil452q6fabyts4tskbusq47l54yokhacdvrdo.py
# Topologically Sorted Source Nodes: [x_32, x_33], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_32 => convolution_18
#   x_33 => gt_12, mul_12, where_12
# Graph fragment:
#   %convolution_18 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_38, %primals_39, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_12 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_18, 0), kwargs = {})
#   %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_18, 0.01), kwargs = {})
#   %where_12 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_12, %convolution_18, %mul_12), kwargs = {})
triton_poi_fused_convolution_leaky_relu_18 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_18', '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_convolution_leaky_relu_18(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bx/cbxupgpftsatjh5mnu5m25yosvv4kcrmlb3ewe5ehw6h62behogi.py
# Topologically Sorted Source Nodes: [x_34, iadd_6, x_35], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_6 => add_6
#   x_34 => convolution_19
#   x_35 => gt_13, mul_13, where_13
# Graph fragment:
#   %convolution_19 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_12, %primals_40, %primals_41, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_6 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_19, %slice_210), kwargs = {})
#   %gt_13 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_6, 0), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_6, 0.01), kwargs = {})
#   %where_13 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_13, %add_6, %mul_13), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_19 = async_compile.triton('triton_poi_fused_add_convolution_leaky_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_19', '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_add_convolution_leaky_relu_19(in_ptr0, in_ptr1, in_ptr2, 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
    x2 = (xindex // 16384)
    x4 = xindex % 16384
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + (65536*x2)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qy/cqyx4ivlwl5e3hnixgpop5rodo77ijdhyu4cykacthp6b6pblqtn.py
# Topologically Sorted Source Nodes: [x_37], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_37 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_20, %where_3], 1), kwargs = {})
triton_poi_fused_cat_20 = async_compile.triton('triton_poi_fused_cat_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=[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_20', '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_20(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 // 1024) % 128
    x0 = xindex % 1024
    x2 = (xindex // 131072)
    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 + (1024*x1) + (65536*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 128, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (1024*((-64) + x1)) + (65536*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/go/cgo77j6qmweqj7pb3bvmfnhkd4xbawaqr62ui6dn7unxpajvsiwi.py
# Topologically Sorted Source Nodes: [x_38, x_39], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_38 => convolution_21
#   x_39 => gt_14, mul_14, where_14
# Graph fragment:
#   %convolution_21 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_44, %primals_45, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_14 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_21, 0), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_21, 0.01), kwargs = {})
#   %where_14 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_14, %convolution_21, %mul_14), kwargs = {})
triton_poi_fused_convolution_leaky_relu_21 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_21', '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_convolution_leaky_relu_21(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uv/cuvfebza4ywja4afrbp7q4jsfdigha6t7ov3i45g66lzrpeeyydk.py
# Topologically Sorted Source Nodes: [x_40, iadd_7, x_41], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_7 => add_7
#   x_40 => convolution_22
#   x_41 => gt_15, mul_15, where_15
# Graph fragment:
#   %convolution_22 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_14, %primals_46, %primals_47, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_7 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_22, %slice_239), kwargs = {})
#   %gt_15 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_7, 0), kwargs = {})
#   %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_7, 0.01), kwargs = {})
#   %where_15 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_15, %add_7, %mul_15), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_22 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_22', '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_add_convolution_leaky_relu_22(in_ptr0, in_ptr1, in_ptr2, 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
    x2 = (xindex // 32768)
    x4 = xindex % 32768
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + (131072*x2)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qm/cqmwkfzmikcapbfklwnx5duzt3m2kkiznvbfkik2i6kizaagzljn.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=3] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_23, %where_1], 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=[1048576], 
    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 = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 64
    x0 = xindex % 4096
    x2 = (xindex // 262144)
    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 + (4096*x1) + (131072*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 64, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (4096*((-32) + x1)) + (131072*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uh/cuhiatd4b3g4fq4hwiu5kyq5ygtth7buqp4idbziynhcgnc5b6kn.py
# Topologically Sorted Source Nodes: [x_44, x_45], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_44 => convolution_24
#   x_45 => gt_16, mul_16, where_16
# Graph fragment:
#   %convolution_24 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_50, %primals_51, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_16 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_24, 0), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_24, 0.01), kwargs = {})
#   %where_16 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_16, %convolution_24, %mul_16), kwargs = {})
triton_poi_fused_convolution_leaky_relu_24 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_24', '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_convolution_leaky_relu_24(in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = 0.0
    tmp5 = tmp3 > tmp4
    tmp6 = 0.01
    tmp7 = tmp3 * tmp6
    tmp8 = tl.where(tmp5, tmp3, tmp7)
    tl.store(out_ptr0 + (x0), tmp5, None)
    tl.store(out_ptr1 + (x0), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hm/chmdwzfjerzpipa7qmmz7fdqjhovmyj2quztbhodv7vkpuqk4l6n.py
# Topologically Sorted Source Nodes: [x_46, iadd_8, x_47], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   iadd_8 => add_8
#   x_46 => convolution_25
#   x_47 => gt_17, mul_17, where_17
# Graph fragment:
#   %convolution_25 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_16, %primals_52, %primals_53, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_8 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_25, %slice_268), kwargs = {})
#   %gt_17 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_8, 0), kwargs = {})
#   %mul_17 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_8, 0.01), kwargs = {})
#   %where_17 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_17, %add_8, %mul_17), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_25 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 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_poi_fused_add_convolution_leaky_relu_25', '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_add_convolution_leaky_relu_25(in_ptr0, in_ptr1, in_ptr2, 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)
    x2 = xindex
    x0 = xindex % 4096
    x1 = (xindex // 4096)
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (x0 + (262144*x1)), None)
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 0.01
    tmp9 = tmp5 * tmp8
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp7, None)
    tl.store(out_ptr1 + (x2), tmp10, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (32, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_7, (32, ), (1, ))
    assert_size_stride(primals_8, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (64, ), (1, ))
    assert_size_stride(primals_12, (64, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_13, (64, ), (1, ))
    assert_size_stride(primals_14, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (128, ), (1, ))
    assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128, ), (1, ))
    assert_size_stride(primals_18, (128, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (256, ), (1, ))
    assert_size_stride(primals_22, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_23, (256, ), (1, ))
    assert_size_stride(primals_24, (256, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_25, (256, ), (1, ))
    assert_size_stride(primals_26, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_27, (256, ), (1, ))
    assert_size_stride(primals_28, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_29, (256, ), (1, ))
    assert_size_stride(primals_30, (256, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_31, (256, ), (1, ))
    assert_size_stride(primals_32, (128, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_33, (128, ), (1, ))
    assert_size_stride(primals_34, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_35, (128, ), (1, ))
    assert_size_stride(primals_36, (128, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_37, (128, ), (1, ))
    assert_size_stride(primals_38, (64, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_39, (64, ), (1, ))
    assert_size_stride(primals_40, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_41, (64, ), (1, ))
    assert_size_stride(primals_42, (64, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_43, (64, ), (1, ))
    assert_size_stride(primals_44, (32, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_45, (32, ), (1, ))
    assert_size_stride(primals_46, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_47, (32, ), (1, ))
    assert_size_stride(primals_48, (32, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_49, (32, ), (1, ))
    assert_size_stride(primals_50, (1, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_51, (1, ), (1, ))
    assert_size_stride(primals_52, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_53, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0.run(buf0, primals_3, buf1, buf2, 524288, grid=grid(524288), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf4 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf5 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x_2, iadd, x_3], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_1.run(buf3, primals_5, primals_1, buf4, buf5, 524288, grid=grid(524288), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf6, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf7, primals_7, 131072, grid=grid(131072), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf9 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf10 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5, x_6], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_3.run(buf8, primals_9, buf9, buf10, 262144, grid=grid(262144), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf12 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf13 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_7, iadd_1, x_8], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_4.run(buf11, primals_11, buf7, buf12, buf13, 262144, grid=grid(262144), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=64, bias=None)
        assert_size_stride(buf14, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf15, primals_13, 65536, grid=grid(65536), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf17 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf16, primals_15, buf17, buf18, 131072, grid=grid(131072), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf20 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf21 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [x_12, iadd_2, x_13], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_7.run(buf19, primals_17, buf15, buf20, buf21, 131072, grid=grid(131072), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_18, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=128, bias=None)
        assert_size_stride(buf22, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf23 = buf22; del buf22  # reuse
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_8.run(buf23, primals_19, 32768, grid=grid(32768), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf25 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_15, x_16], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_9.run(buf24, primals_21, buf25, buf26, 65536, grid=grid(65536), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [x_17], Original ATen: [aten.convolution]
        buf27 = extern_kernels.convolution(buf26, primals_22, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        buf29 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [x_17, iadd_3, x_18], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_10.run(buf27, primals_23, buf23, buf28, buf29, 65536, grid=grid(65536), stream=stream0)
        del buf27
        del primals_23
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, primals_24, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=256, bias=None)
        assert_size_stride(buf30, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_11.run(buf31, primals_25, 16384, grid=grid(16384), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [x_20], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf33 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool)
        buf34 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_20, x_21], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_12.run(buf32, primals_27, buf33, buf34, 16384, grid=grid(16384), stream=stream0)
        del primals_27
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.convolution]
        buf35 = extern_kernels.convolution(buf34, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf36 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool)
        buf37 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [x_22, iadd_4, x_23], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_13.run(buf35, primals_29, buf31, buf36, buf37, 16384, grid=grid(16384), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [x_24], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_30, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=256, bias=None)
        assert_size_stride(buf38, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf39 = reinterpret_tensor(buf19, (4, 512, 8, 8), (32768, 64, 8, 1), 0); del buf19  # reuse
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.cat]
        triton_poi_fused_cat_14.run(buf38, primals_31, buf29, buf39, 131072, grid=grid(131072), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [x_26], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, primals_32, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf41 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool)
        buf42 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_26, x_27], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_15.run(buf40, primals_33, buf41, buf42, 32768, grid=grid(32768), stream=stream0)
        del primals_33
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.convolution]
        buf43 = extern_kernels.convolution(buf42, primals_34, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf44 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool)
        buf45 = buf40; del buf40  # reuse
        # Topologically Sorted Source Nodes: [x_28, iadd_5, x_29], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_16.run(buf43, primals_35, buf39, buf44, buf45, 32768, grid=grid(32768), stream=stream0)
        del buf43
        del primals_35
        # Topologically Sorted Source Nodes: [x_30], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_36, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=128, bias=None)
        assert_size_stride(buf46, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf47 = reinterpret_tensor(buf11, (4, 256, 16, 16), (65536, 256, 16, 1), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [x_31], Original ATen: [aten.cat]
        triton_poi_fused_cat_17.run(buf46, primals_37, buf21, buf47, 262144, grid=grid(262144), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [x_32], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf49 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool)
        buf50 = reinterpret_tensor(buf38, (4, 64, 16, 16), (16384, 256, 16, 1), 0); del buf38  # reuse
        # Topologically Sorted Source Nodes: [x_32, x_33], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_18.run(buf48, primals_39, buf49, buf50, 65536, grid=grid(65536), stream=stream0)
        del primals_39
        # Topologically Sorted Source Nodes: [x_34], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf50, primals_40, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf52 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool)
        buf53 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [x_34, iadd_6, x_35], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_19.run(buf51, primals_41, buf47, buf52, buf53, 65536, grid=grid(65536), stream=stream0)
        del buf51
        del primals_41
        # Topologically Sorted Source Nodes: [x_36], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf53, primals_42, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=64, bias=None)
        assert_size_stride(buf54, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf55 = reinterpret_tensor(buf3, (4, 128, 32, 32), (131072, 1024, 32, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [x_37], Original ATen: [aten.cat]
        triton_poi_fused_cat_20.run(buf54, primals_43, buf13, buf55, 524288, grid=grid(524288), stream=stream0)
        del buf54
        del primals_43
        # Topologically Sorted Source Nodes: [x_38], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf55, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf57 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.bool)
        buf58 = reinterpret_tensor(buf46, (4, 32, 32, 32), (32768, 1024, 32, 1), 0); del buf46  # reuse
        # Topologically Sorted Source Nodes: [x_38, x_39], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_21.run(buf56, primals_45, buf57, buf58, 131072, grid=grid(131072), stream=stream0)
        del primals_45
        # Topologically Sorted Source Nodes: [x_40], Original ATen: [aten.convolution]
        buf59 = extern_kernels.convolution(buf58, primals_46, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf60 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.bool)
        buf61 = buf56; del buf56  # reuse
        # Topologically Sorted Source Nodes: [x_40, iadd_7, x_41], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_22.run(buf59, primals_47, buf55, buf60, buf61, 131072, grid=grid(131072), stream=stream0)
        del buf59
        del primals_47
        # Topologically Sorted Source Nodes: [x_42], Original ATen: [aten.convolution]
        buf62 = extern_kernels.convolution(buf61, primals_48, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf62, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf63 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_43], Original ATen: [aten.cat]
        triton_poi_fused_cat_23.run(buf62, primals_49, buf5, buf63, 1048576, grid=grid(1048576), stream=stream0)
        del buf62
        del primals_49
        # Topologically Sorted Source Nodes: [x_44], Original ATen: [aten.convolution]
        buf64 = extern_kernels.convolution(buf63, primals_50, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf65 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1), torch.bool)
        buf66 = reinterpret_tensor(buf35, (4, 1, 64, 64), (4096, 4096, 64, 1), 0); del buf35  # reuse
        # Topologically Sorted Source Nodes: [x_44, x_45], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_24.run(buf64, primals_51, buf65, buf66, 16384, grid=grid(16384), stream=stream0)
        del primals_51
        # Topologically Sorted Source Nodes: [x_46], Original ATen: [aten.convolution]
        buf67 = extern_kernels.convolution(buf66, primals_52, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf68 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1), torch.bool)
        buf69 = buf64; del buf64  # reuse
        # Topologically Sorted Source Nodes: [x_46, iadd_8, x_47], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_25.run(buf67, primals_53, buf63, buf68, buf69, 16384, grid=grid(16384), stream=stream0)
        del buf67
        del primals_53
    return (buf69, primals_1, primals_2, 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, buf1, buf2, buf4, buf5, buf7, buf9, buf10, buf12, buf13, buf15, buf17, buf18, buf20, buf21, buf23, buf25, buf26, buf28, buf29, buf31, buf33, buf34, buf36, buf37, buf39, buf41, buf42, buf44, buf45, buf47, buf49, buf50, buf52, buf53, buf55, buf57, buf58, buf60, buf61, buf63, buf65, buf66, buf68, )


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((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((64, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((256, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((256, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((128, 512, 3, 3), (4608, 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((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((128, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((64, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((64, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((32, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((32, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((1, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_53 = 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])
    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 torch.nn as nn


class ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels, dropout=False, norm=
        'batch', residual=True, activation='leakyrelu', transpose=False):
        super(ConvBlock, self).__init__()
        self.dropout = dropout
        self.residual = residual
        self.activation = activation
        self.transpose = transpose
        if self.dropout:
            self.dropout1 = nn.Dropout2d(p=0.05)
            self.dropout2 = nn.Dropout2d(p=0.05)
        self.norm1 = None
        self.norm2 = None
        if norm == 'batch':
            self.norm1 = nn.BatchNorm2d(out_channels)
            self.norm2 = nn.BatchNorm2d(out_channels)
        elif norm == 'instance':
            self.norm1 = nn.InstanceNorm2d(out_channels, affine=True)
            self.norm2 = nn.InstanceNorm2d(out_channels, affine=True)
        elif norm == 'mixed':
            self.norm1 = nn.BatchNorm2d(out_channels, affine=True)
            self.norm2 = nn.InstanceNorm2d(out_channels, affine=True)
        if self.transpose:
            self.conv1 = nn.ConvTranspose2d(in_channels, out_channels,
                kernel_size=3, padding=1)
            self.conv2 = nn.ConvTranspose2d(out_channels, out_channels,
                kernel_size=3, padding=1)
        else:
            self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
                padding=1)
            self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=
                3, padding=1)
        if self.activation == 'relu':
            self.actfun1 = nn.ReLU()
            self.actfun2 = nn.ReLU()
        elif self.activation == 'leakyrelu':
            self.actfun1 = nn.LeakyReLU()
            self.actfun2 = nn.LeakyReLU()
        elif self.activation == 'elu':
            self.actfun1 = nn.ELU()
            self.actfun2 = nn.ELU()
        elif self.activation == 'selu':
            self.actfun1 = nn.SELU()
            self.actfun2 = nn.SELU()

    def forward(self, x):
        ox = x
        x = self.conv1(x)
        if self.dropout:
            x = self.dropout1(x)
        if self.norm1:
            x = self.norm1(x)
        x = self.actfun1(x)
        x = self.conv2(x)
        if self.dropout:
            x = self.dropout2(x)
        if self.norm2:
            x = self.norm2(x)
        if self.residual:
            x[:, 0:min(ox.shape[1], x.shape[1]), :, :] += ox[:, 0:min(ox.
                shape[1], x.shape[1]), :, :]
        x = self.actfun2(x)
        return x


class Unet(nn.Module):

    def __init__(self, n_channel_in=1, n_channel_out=1, residual=False,
        down='conv', up='tconv', activation='selu'):
        super(Unet, self).__init__()
        self.residual = residual
        if down == 'maxpool':
            self.down1 = nn.MaxPool2d(kernel_size=2)
            self.down2 = nn.MaxPool2d(kernel_size=2)
            self.down3 = nn.MaxPool2d(kernel_size=2)
            self.down4 = nn.MaxPool2d(kernel_size=2)
        elif down == 'avgpool':
            self.down1 = nn.AvgPool2d(kernel_size=2)
            self.down2 = nn.AvgPool2d(kernel_size=2)
            self.down3 = nn.AvgPool2d(kernel_size=2)
            self.down4 = nn.AvgPool2d(kernel_size=2)
        elif down == 'conv':
            self.down1 = nn.Conv2d(32, 32, kernel_size=2, stride=2, groups=32)
            self.down2 = nn.Conv2d(64, 64, kernel_size=2, stride=2, groups=64)
            self.down3 = nn.Conv2d(128, 128, kernel_size=2, stride=2,
                groups=128)
            self.down4 = nn.Conv2d(256, 256, kernel_size=2, stride=2,
                groups=256)
            self.down1.weight.data = 0.01 * self.down1.weight.data + 0.25
            self.down2.weight.data = 0.01 * self.down2.weight.data + 0.25
            self.down3.weight.data = 0.01 * self.down3.weight.data + 0.25
            self.down4.weight.data = 0.01 * self.down4.weight.data + 0.25
            self.down1.bias.data = 0.01 * self.down1.bias.data + 0
            self.down2.bias.data = 0.01 * self.down2.bias.data + 0
            self.down3.bias.data = 0.01 * self.down3.bias.data + 0
            self.down4.bias.data = 0.01 * self.down4.bias.data + 0
        if up == 'bilinear' or up == 'nearest':
            self.up1 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
            self.up2 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
            self.up3 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
            self.up4 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
        elif up == 'tconv':
            self.up1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2,
                groups=256)
            self.up2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2,
                groups=128)
            self.up3 = nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2,
                groups=64)
            self.up4 = nn.ConvTranspose2d(32, 32, kernel_size=2, stride=2,
                groups=32)
            self.up1.weight.data = 0.01 * self.up1.weight.data + 0.25
            self.up2.weight.data = 0.01 * self.up2.weight.data + 0.25
            self.up3.weight.data = 0.01 * self.up3.weight.data + 0.25
            self.up4.weight.data = 0.01 * self.up4.weight.data + 0.25
            self.up1.bias.data = 0.01 * self.up1.bias.data + 0
            self.up2.bias.data = 0.01 * self.up2.bias.data + 0
            self.up3.bias.data = 0.01 * self.up3.bias.data + 0
            self.up4.bias.data = 0.01 * self.up4.bias.data + 0
        self.conv1 = ConvBlock(n_channel_in, 32, residual, activation)
        self.conv2 = ConvBlock(32, 64, residual, activation)
        self.conv3 = ConvBlock(64, 128, residual, activation)
        self.conv4 = ConvBlock(128, 256, residual, activation)
        self.conv5 = ConvBlock(256, 256, residual, activation)
        self.conv6 = ConvBlock(2 * 256, 128, residual, activation)
        self.conv7 = ConvBlock(2 * 128, 64, residual, activation)
        self.conv8 = ConvBlock(2 * 64, 32, residual, activation)
        self.conv9 = ConvBlock(2 * 32, n_channel_out, residual, activation)
        if self.residual:
            self.convres = ConvBlock(n_channel_in, n_channel_out, residual,
                activation)

    def forward(self, x):
        c0 = x
        c1 = self.conv1(x)
        x = self.down1(c1)
        c2 = self.conv2(x)
        x = self.down2(c2)
        c3 = self.conv3(x)
        x = self.down3(c3)
        c4 = self.conv4(x)
        x = self.down4(c4)
        x = self.conv5(x)
        x = self.up1(x)
        x = torch.cat([x, c4], 1)
        x = self.conv6(x)
        x = self.up2(x)
        x = torch.cat([x, c3], 1)
        x = self.conv7(x)
        x = self.up3(x)
        x = torch.cat([x, c2], 1)
        x = self.conv8(x)
        x = self.up4(x)
        x = torch.cat([x, c1], 1)
        x = self.conv9(x)
        if self.residual:
            x = torch.add(x, self.convres(c0))
        return x


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
import torch.nn as nn
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_leaky_relu_0(in_ptr0, in_ptr1, 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 // 4096 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_1(in_ptr0, in_ptr1, in_ptr2,
    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)
    x1 = xindex // 4096 % 32
    x3 = xindex
    x0 = xindex % 4096
    x2 = xindex // 131072
    tmp21 = tl.load(in_ptr0 + x3, None)
    tmp22 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + x3, tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + x1, tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x0 + 4096 * x2), tmp3, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + x3, tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + x1, tmp2, eviction_policy='evict_last', other=0.0
        )
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x0 + 4096 * x2), tmp2, eviction_policy=
        'evict_last', other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + x3, tmp27, None)
    tl.store(out_ptr1 + x3, tmp30, None)


@triton.jit
def triton_poi_fused_convolution_2(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_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, 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 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_4(in_ptr0, in_ptr1, in_ptr2,
    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)
    x1 = xindex // 1024 % 64
    x3 = xindex
    x2 = xindex // 65536
    x4 = xindex % 65536
    tmp21 = tl.load(in_ptr0 + x3, None)
    tmp22 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 32, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + x3, tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + x1, tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x4 + 32768 * x2), tmp3, other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + x3, tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + x1, tmp2, eviction_policy='evict_last', other=0.0
        )
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x4 + 32768 * x2), tmp2, other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + x3, tmp27, None)
    tl.store(out_ptr1 + x3, tmp30, 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 // 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_poi_fused_convolution_leaky_relu_6(in_ptr0, in_ptr1, 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 % 128
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_7(in_ptr0, in_ptr1, in_ptr2,
    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)
    x1 = xindex // 256 % 128
    x3 = xindex
    x2 = xindex // 32768
    x4 = xindex % 32768
    tmp21 = tl.load(in_ptr0 + x3, None)
    tmp22 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 64, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + x3, tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + x1, tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x4 + 16384 * x2), tmp3, other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + x3, tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + x1, tmp2, eviction_policy='evict_last', other=0.0
        )
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x4 + 16384 * x2), tmp2, other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + x3, tmp27, None)
    tl.store(out_ptr1 + x3, tmp30, None)


@triton.jit
def triton_poi_fused_convolution_8(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_poi_fused_convolution_leaky_relu_9(in_ptr0, in_ptr1, 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 % 256
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_10(in_ptr0, in_ptr1,
    in_ptr2, 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)
    x1 = xindex // 64 % 256
    x3 = xindex
    x2 = xindex // 16384
    x4 = xindex % 16384
    tmp21 = tl.load(in_ptr0 + x3, None)
    tmp22 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 128, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tmp2 & tmp2
    tmp4 = tl.load(in_ptr0 + x3, tmp3, other=0.0)
    tmp5 = tl.load(in_ptr1 + x1, tmp3, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.load(in_ptr2 + (x4 + 8192 * x2), tmp3, other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp3, tmp8, tmp9)
    tmp11 = tl.load(in_ptr0 + x3, tmp2, other=0.0)
    tmp12 = tl.load(in_ptr1 + x1, tmp2, eviction_policy='evict_last', other=0.0
        )
    tmp13 = tmp11 + tmp12
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tmp17 = tl.load(in_ptr2 + (x4 + 8192 * x2), tmp2, other=0.0)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp2, tmp18, tmp19)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.where(tmp2, tmp20, tmp23)
    tmp25 = tl.where(tmp2, tmp16, tmp24)
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(out_ptr0 + x3, tmp27, None)
    tl.store(out_ptr1 + x3, tmp30, None)


@triton.jit
def triton_poi_fused_convolution_11(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_poi_fused_convolution_leaky_relu_12(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_13(in_ptr0, in_ptr1,
    in_ptr2, 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 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_cat_14(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 % 512
    x0 = xindex % 64
    x2 = xindex // 32768
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1 + 16384 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 64 * (-256 + x1) + 16384 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_15(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_16(in_ptr0, in_ptr1,
    in_ptr2, 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
    x2 = xindex // 8192
    x4 = xindex % 8192
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + 32768 * x2), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_cat_17(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 % 256
    x0 = xindex % 256
    x2 = xindex // 65536
    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 + 256 * x1 + 32768 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 256 * (-128 + x1) + 32768 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_18(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_19(in_ptr0, in_ptr1,
    in_ptr2, 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
    x2 = xindex // 16384
    x4 = xindex % 16384
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + 65536 * x2), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp9, None)


@triton.jit
def triton_poi_fused_cat_20(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 % 128
    x0 = xindex % 1024
    x2 = xindex // 131072
    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 + 1024 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 1024 * (-64 + x1) + 65536 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_21(in_ptr0, in_ptr1, 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')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_22(in_ptr0, in_ptr1,
    in_ptr2, 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
    x2 = xindex // 32768
    x4 = xindex % 32768
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + 131072 * x2), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp9, None)


@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 // 4096 % 64
    x0 = xindex % 4096
    x2 = xindex // 262144
    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 + 4096 * x1 + 131072 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 64, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 4096 * (-32 + x1) + 131072 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_24(in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = 0.0
    tmp5 = tmp3 > tmp4
    tmp6 = 0.01
    tmp7 = tmp3 * tmp6
    tmp8 = tl.where(tmp5, tmp3, tmp7)
    tl.store(out_ptr0 + x0, tmp5, None)
    tl.store(out_ptr1 + x0, tmp8, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_25(in_ptr0, in_ptr1,
    in_ptr2, 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)
    x2 = xindex
    x0 = xindex % 4096
    x1 = xindex // 4096
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (x0 + 262144 * x1), None)
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 0.01
    tmp9 = tmp5 * tmp8
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp7, None)
    tl.store(out_ptr1 + x2, tmp10, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (64, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (128,), (1,))
    assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (128, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (256,), (1,))
    assert_size_stride(primals_22, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_23, (256,), (1,))
    assert_size_stride(primals_24, (256, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_25, (256,), (1,))
    assert_size_stride(primals_26, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_27, (256,), (1,))
    assert_size_stride(primals_28, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_29, (256,), (1,))
    assert_size_stride(primals_30, (256, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_31, (256,), (1,))
    assert_size_stride(primals_32, (128, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_33, (128,), (1,))
    assert_size_stride(primals_34, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_35, (128,), (1,))
    assert_size_stride(primals_36, (128, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_37, (128,), (1,))
    assert_size_stride(primals_38, (64, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_39, (64,), (1,))
    assert_size_stride(primals_40, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_41, (64,), (1,))
    assert_size_stride(primals_42, (64, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_43, (64,), (1,))
    assert_size_stride(primals_44, (32, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_45, (32,), (1,))
    assert_size_stride(primals_46, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_47, (32,), (1,))
    assert_size_stride(primals_48, (32, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(primals_49, (32,), (1,))
    assert_size_stride(primals_50, (1, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_51, (1,), (1,))
    assert_size_stride(primals_52, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_53, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf2 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf0,
            primals_3, buf1, buf2, 524288, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf4 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf5 = buf0
        del buf0
        triton_poi_fused_add_convolution_leaky_relu_1[grid(524288)](buf3,
            primals_5, primals_1, buf4, buf5, 524288, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf6, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_2[grid(131072)](buf7, primals_7, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf9 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf10 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_3[grid(262144)](buf8,
            primals_9, buf9, buf10, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_9
        buf11 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf12 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf13 = buf8
        del buf8
        triton_poi_fused_add_convolution_leaky_relu_4[grid(262144)](buf11,
            primals_11, buf7, buf12, buf13, 262144, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_11
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=64, bias=None)
        assert_size_stride(buf14, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_5[grid(65536)](buf15, primals_13, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_13
        buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf17 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_6[grid(131072)](buf16,
            primals_15, buf17, buf18, 131072, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_15
        buf19 = extern_kernels.convolution(buf18, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf20 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf21 = buf16
        del buf16
        triton_poi_fused_add_convolution_leaky_relu_7[grid(131072)](buf19,
            primals_17, buf15, buf20, buf21, 131072, XBLOCK=512, num_warps=
            8, num_stages=1)
        del primals_17
        buf22 = extern_kernels.convolution(buf21, primals_18, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=128, bias=None)
        assert_size_stride(buf22, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf23 = buf22
        del buf22
        triton_poi_fused_convolution_8[grid(32768)](buf23, primals_19, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_19
        buf24 = extern_kernels.convolution(buf23, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf25 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_9[grid(65536)](buf24,
            primals_21, buf25, buf26, 65536, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_21
        buf27 = extern_kernels.convolution(buf26, primals_22, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        buf29 = buf24
        del buf24
        triton_poi_fused_add_convolution_leaky_relu_10[grid(65536)](buf27,
            primals_23, buf23, buf28, buf29, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf27
        del primals_23
        buf30 = extern_kernels.convolution(buf29, primals_24, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=256, bias=None)
        assert_size_stride(buf30, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_11[grid(16384)](buf31, primals_25, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_25
        buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf33 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool
            )
        buf34 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_12[grid(16384)](buf32,
            primals_27, buf33, buf34, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_27
        buf35 = extern_kernels.convolution(buf34, primals_28, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf36 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool
            )
        buf37 = buf32
        del buf32
        triton_poi_fused_add_convolution_leaky_relu_13[grid(16384)](buf35,
            primals_29, buf31, buf36, buf37, 16384, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_29
        buf38 = extern_kernels.convolution(buf37, primals_30, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=256, bias=None)
        assert_size_stride(buf38, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf39 = reinterpret_tensor(buf19, (4, 512, 8, 8), (32768, 64, 8, 1), 0)
        del buf19
        triton_poi_fused_cat_14[grid(131072)](buf38, primals_31, buf29,
            buf39, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_31
        buf40 = extern_kernels.convolution(buf39, primals_32, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf41 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool
            )
        buf42 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_15[grid(32768)](buf40,
            primals_33, buf41, buf42, 32768, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_33
        buf43 = extern_kernels.convolution(buf42, primals_34, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf44 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool
            )
        buf45 = buf40
        del buf40
        triton_poi_fused_add_convolution_leaky_relu_16[grid(32768)](buf43,
            primals_35, buf39, buf44, buf45, 32768, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf43
        del primals_35
        buf46 = extern_kernels.convolution(buf45, primals_36, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=128, bias=None)
        assert_size_stride(buf46, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf47 = reinterpret_tensor(buf11, (4, 256, 16, 16), (65536, 256, 16,
            1), 0)
        del buf11
        triton_poi_fused_cat_17[grid(262144)](buf46, primals_37, buf21,
            buf47, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_37
        buf48 = extern_kernels.convolution(buf47, primals_38, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf49 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.bool)
        buf50 = reinterpret_tensor(buf38, (4, 64, 16, 16), (16384, 256, 16,
            1), 0)
        del buf38
        triton_poi_fused_convolution_leaky_relu_18[grid(65536)](buf48,
            primals_39, buf49, buf50, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_39
        buf51 = extern_kernels.convolution(buf50, primals_40, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf52 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.bool)
        buf53 = buf48
        del buf48
        triton_poi_fused_add_convolution_leaky_relu_19[grid(65536)](buf51,
            primals_41, buf47, buf52, buf53, 65536, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf51
        del primals_41
        buf54 = extern_kernels.convolution(buf53, primals_42, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=64, bias=None)
        assert_size_stride(buf54, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf55 = reinterpret_tensor(buf3, (4, 128, 32, 32), (131072, 1024, 
            32, 1), 0)
        del buf3
        triton_poi_fused_cat_20[grid(524288)](buf54, primals_43, buf13,
            buf55, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del buf54
        del primals_43
        buf56 = extern_kernels.convolution(buf55, primals_44, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf57 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.bool)
        buf58 = reinterpret_tensor(buf46, (4, 32, 32, 32), (32768, 1024, 32,
            1), 0)
        del buf46
        triton_poi_fused_convolution_leaky_relu_21[grid(131072)](buf56,
            primals_45, buf57, buf58, 131072, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_45
        buf59 = extern_kernels.convolution(buf58, primals_46, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf60 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.bool)
        buf61 = buf56
        del buf56
        triton_poi_fused_add_convolution_leaky_relu_22[grid(131072)](buf59,
            primals_47, buf55, buf60, buf61, 131072, XBLOCK=1024, num_warps
            =4, num_stages=1)
        del buf59
        del primals_47
        buf62 = extern_kernels.convolution(buf61, primals_48, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf62, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf63 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_23[grid(1048576)](buf62, primals_49, buf5,
            buf63, 1048576, XBLOCK=512, num_warps=8, num_stages=1)
        del buf62
        del primals_49
        buf64 = extern_kernels.convolution(buf63, primals_50, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf65 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
            torch.bool)
        buf66 = reinterpret_tensor(buf35, (4, 1, 64, 64), (4096, 4096, 64, 
            1), 0)
        del buf35
        triton_poi_fused_convolution_leaky_relu_24[grid(16384)](buf64,
            primals_51, buf65, buf66, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_51
        buf67 = extern_kernels.convolution(buf66, primals_52, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf68 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
            torch.bool)
        buf69 = buf64
        del buf64
        triton_poi_fused_add_convolution_leaky_relu_25[grid(16384)](buf67,
            primals_53, buf63, buf68, buf69, 16384, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf67
        del primals_53
    return (buf69, primals_1, primals_2, 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, buf1, buf2, buf4, buf5, buf7, buf9, buf10,
        buf12, buf13, buf15, buf17, buf18, buf20, buf21, buf23, buf25,
        buf26, buf28, buf29, buf31, buf33, buf34, buf36, buf37, buf39,
        buf41, buf42, buf44, buf45, buf47, buf49, buf50, buf52, buf53,
        buf55, buf57, buf58, buf60, buf61, buf63, buf65, buf66, buf68)


class ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels, dropout=False, norm=
        'batch', residual=True, activation='leakyrelu', transpose=False):
        super(ConvBlock, self).__init__()
        self.dropout = dropout
        self.residual = residual
        self.activation = activation
        self.transpose = transpose
        if self.dropout:
            self.dropout1 = nn.Dropout2d(p=0.05)
            self.dropout2 = nn.Dropout2d(p=0.05)
        self.norm1 = None
        self.norm2 = None
        if norm == 'batch':
            self.norm1 = nn.BatchNorm2d(out_channels)
            self.norm2 = nn.BatchNorm2d(out_channels)
        elif norm == 'instance':
            self.norm1 = nn.InstanceNorm2d(out_channels, affine=True)
            self.norm2 = nn.InstanceNorm2d(out_channels, affine=True)
        elif norm == 'mixed':
            self.norm1 = nn.BatchNorm2d(out_channels, affine=True)
            self.norm2 = nn.InstanceNorm2d(out_channels, affine=True)
        if self.transpose:
            self.conv1 = nn.ConvTranspose2d(in_channels, out_channels,
                kernel_size=3, padding=1)
            self.conv2 = nn.ConvTranspose2d(out_channels, out_channels,
                kernel_size=3, padding=1)
        else:
            self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
                padding=1)
            self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=
                3, padding=1)
        if self.activation == 'relu':
            self.actfun1 = nn.ReLU()
            self.actfun2 = nn.ReLU()
        elif self.activation == 'leakyrelu':
            self.actfun1 = nn.LeakyReLU()
            self.actfun2 = nn.LeakyReLU()
        elif self.activation == 'elu':
            self.actfun1 = nn.ELU()
            self.actfun2 = nn.ELU()
        elif self.activation == 'selu':
            self.actfun1 = nn.SELU()
            self.actfun2 = nn.SELU()

    def forward(self, x):
        ox = x
        x = self.conv1(x)
        if self.dropout:
            x = self.dropout1(x)
        if self.norm1:
            x = self.norm1(x)
        x = self.actfun1(x)
        x = self.conv2(x)
        if self.dropout:
            x = self.dropout2(x)
        if self.norm2:
            x = self.norm2(x)
        if self.residual:
            x[:, 0:min(ox.shape[1], x.shape[1]), :, :] += ox[:, 0:min(ox.
                shape[1], x.shape[1]), :, :]
        x = self.actfun2(x)
        return x


class UnetNew(nn.Module):

    def __init__(self, n_channel_in=1, n_channel_out=1, residual=False,
        down='conv', up='tconv', activation='selu'):
        super(UnetNew, self).__init__()
        self.residual = residual
        if down == 'maxpool':
            self.down1 = nn.MaxPool2d(kernel_size=2)
            self.down2 = nn.MaxPool2d(kernel_size=2)
            self.down3 = nn.MaxPool2d(kernel_size=2)
            self.down4 = nn.MaxPool2d(kernel_size=2)
        elif down == 'avgpool':
            self.down1 = nn.AvgPool2d(kernel_size=2)
            self.down2 = nn.AvgPool2d(kernel_size=2)
            self.down3 = nn.AvgPool2d(kernel_size=2)
            self.down4 = nn.AvgPool2d(kernel_size=2)
        elif down == 'conv':
            self.down1 = nn.Conv2d(32, 32, kernel_size=2, stride=2, groups=32)
            self.down2 = nn.Conv2d(64, 64, kernel_size=2, stride=2, groups=64)
            self.down3 = nn.Conv2d(128, 128, kernel_size=2, stride=2,
                groups=128)
            self.down4 = nn.Conv2d(256, 256, kernel_size=2, stride=2,
                groups=256)
            self.down1.weight.data = 0.01 * self.down1.weight.data + 0.25
            self.down2.weight.data = 0.01 * self.down2.weight.data + 0.25
            self.down3.weight.data = 0.01 * self.down3.weight.data + 0.25
            self.down4.weight.data = 0.01 * self.down4.weight.data + 0.25
            self.down1.bias.data = 0.01 * self.down1.bias.data + 0
            self.down2.bias.data = 0.01 * self.down2.bias.data + 0
            self.down3.bias.data = 0.01 * self.down3.bias.data + 0
            self.down4.bias.data = 0.01 * self.down4.bias.data + 0
        if up == 'bilinear' or up == 'nearest':
            self.up1 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
            self.up2 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
            self.up3 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
            self.up4 = lambda x: nn.functional.interpolate(x, mode=up,
                scale_factor=2)
        elif up == 'tconv':
            self.up1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2,
                groups=256)
            self.up2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2,
                groups=128)
            self.up3 = nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2,
                groups=64)
            self.up4 = nn.ConvTranspose2d(32, 32, kernel_size=2, stride=2,
                groups=32)
            self.up1.weight.data = 0.01 * self.up1.weight.data + 0.25
            self.up2.weight.data = 0.01 * self.up2.weight.data + 0.25
            self.up3.weight.data = 0.01 * self.up3.weight.data + 0.25
            self.up4.weight.data = 0.01 * self.up4.weight.data + 0.25
            self.up1.bias.data = 0.01 * self.up1.bias.data + 0
            self.up2.bias.data = 0.01 * self.up2.bias.data + 0
            self.up3.bias.data = 0.01 * self.up3.bias.data + 0
            self.up4.bias.data = 0.01 * self.up4.bias.data + 0
        self.conv1 = ConvBlock(n_channel_in, 32, residual, activation)
        self.conv2 = ConvBlock(32, 64, residual, activation)
        self.conv3 = ConvBlock(64, 128, residual, activation)
        self.conv4 = ConvBlock(128, 256, residual, activation)
        self.conv5 = ConvBlock(256, 256, residual, activation)
        self.conv6 = ConvBlock(2 * 256, 128, residual, activation)
        self.conv7 = ConvBlock(2 * 128, 64, residual, activation)
        self.conv8 = ConvBlock(2 * 64, 32, residual, activation)
        self.conv9 = ConvBlock(2 * 32, n_channel_out, residual, activation)
        if self.residual:
            self.convres = ConvBlock(n_channel_in, n_channel_out, residual,
                activation)

    def forward(self, input_0):
        primals_6 = self.down1.weight
        primals_3 = self.down1.bias
        primals_12 = self.down2.weight
        primals_9 = self.down2.bias
        primals_18 = self.down3.weight
        primals_15 = self.down3.bias
        primals_24 = self.down4.weight
        primals_21 = self.down4.bias
        primals_30 = self.up1.weight
        primals_23 = self.up1.bias
        primals_36 = self.up2.weight
        primals_17 = self.up2.bias
        primals_42 = self.up3.weight
        primals_11 = self.up3.bias
        primals_48 = self.up4.weight
        primals_5 = self.up4.bias
        primals_2 = self.conv1.conv1.weight
        primals_7 = self.conv1.conv1.bias
        primals_4 = self.conv1.conv2.weight
        primals_45 = self.conv1.conv2.bias
        primals_8 = self.conv2.conv1.weight
        primals_13 = self.conv2.conv1.bias
        primals_10 = self.conv2.conv2.weight
        primals_39 = self.conv2.conv2.bias
        primals_14 = self.conv3.conv1.weight
        primals_19 = self.conv3.conv1.bias
        primals_16 = self.conv3.conv2.weight
        primals_33 = self.conv3.conv2.bias
        primals_20 = self.conv4.conv1.weight
        primals_25 = self.conv4.conv1.bias
        primals_22 = self.conv4.conv2.weight
        primals_27 = self.conv4.conv2.bias
        primals_26 = self.conv5.conv1.weight
        primals_29 = self.conv5.conv1.bias
        primals_28 = self.conv5.conv2.weight
        primals_31 = self.conv5.conv2.bias
        primals_32 = self.conv6.conv1.weight
        primals_35 = self.conv6.conv1.bias
        primals_34 = self.conv6.conv2.weight
        primals_37 = self.conv6.conv2.bias
        primals_38 = self.conv7.conv1.weight
        primals_41 = self.conv7.conv1.bias
        primals_40 = self.conv7.conv2.weight
        primals_43 = self.conv7.conv2.bias
        primals_44 = self.conv8.conv1.weight
        primals_47 = self.conv8.conv1.bias
        primals_46 = self.conv8.conv2.weight
        primals_49 = self.conv8.conv2.bias
        primals_50 = self.conv9.conv1.weight
        primals_51 = self.conv9.conv1.bias
        primals_52 = self.conv9.conv2.weight
        primals_53 = self.conv9.conv2.bias
        primals_1 = 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])
        return output[0]
