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

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


# kernel path: runs/run_shard_4/inductor_cache/7g/c7gdibvkv66wahy4qteufqgghcjbacvogl535jigu4ill24lb4pn.py
# Topologically Sorted Source Nodes: [x, out1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   out1 => gt, mul, where
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %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, 0.2), kwargs = {})
#   %where : [num_users=3] = 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=[1048576], 
    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 = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 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 = 0.2
    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_4/inductor_cache/ag/cagavkog5o27eoekhbihlksvmikgcymf2wq5rb2url3hsqeryznv.py
# Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d => _low_memory_max_pool2d_offsets_to_indices, _low_memory_max_pool2d_with_offsets, getitem
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%where, [2, 2], [2, 2], [1, 1], [1, 1], False), kwargs = {})
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %_low_memory_max_pool2d_offsets_to_indices : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_offsets_to_indices.default](args = (%getitem_1, 2, 64, [2, 2], [1, 1]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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: '*i64', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 33) % 33
    x0 = xindex % 33
    x2 = (xindex // 1089)
    x4 = xindex % 1089
    x5 = xindex
    tmp0 = (-1) + (2*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-65) + (2*x0) + (128*x1) + (4096*x2)), tmp10 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-64) + (2*x0) + (128*x1) + (4096*x2)), tmp16 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2*x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + ((-1) + (2*x0) + (128*x1) + (4096*x2)), tmp23 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + ((2*x0) + (128*x1) + (4096*x2)), tmp26 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + (x4 + (1120*x2)), tmp28, xmask)
    tl.store(out_ptr2 + (x5), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tq/ctq423m3higghu5rwkfx27kes7coot6nputhwykhm36gprgzoadl.py
# Topologically Sorted Source Nodes: [x_2, out2], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   out2 => gt_1, mul_1, where_1
#   x_2 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.2), kwargs = {})
#   %where_1 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
triton_poi_fused_convolution_leaky_relu_2 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[1048576], 
    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_2', '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_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 1089) % 128
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7l/c7l2sp6zoaduxwlfi3vie6gac7mr6pr7kmcphj4m5yqchvg7bch5.py
# Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_1 => _low_memory_max_pool2d_offsets_to_indices_1, _low_memory_max_pool2d_with_offsets_1, getitem_2
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%where_1, [2, 2], [2, 2], [1, 1], [1, 1], False), kwargs = {})
#   %getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 0), kwargs = {})
#   %_low_memory_max_pool2d_offsets_to_indices_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_offsets_to_indices.default](args = (%getitem_3, 2, 33, [2, 2], [1, 1]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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: '*i64', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 17) % 17
    x0 = xindex % 17
    x2 = (xindex // 289)
    x4 = xindex
    tmp0 = (-1) + (2*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 33, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-34) + (2*x0) + (66*x1) + (1089*x2)), tmp10 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-33) + (2*x0) + (66*x1) + (1089*x2)), tmp16 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2*x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + ((-1) + (2*x0) + (66*x1) + (1089*x2)), tmp23 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + ((2*x0) + (66*x1) + (1089*x2)), tmp26 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + (x4), tmp28, xmask)
    tl.store(out_ptr2 + (x4), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ld/cldyj33m7sflwes7s2y5s5gx5s4o6lb6uxhxygwb4z36mxzzja7o.py
# Topologically Sorted Source Nodes: [x_4, out3], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   out3 => gt_2, mul_2, where_2
#   x_4 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_6, %primals_7, [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_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 0.2), kwargs = {})
#   %where_2 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
triton_poi_fused_convolution_leaky_relu_4 = async_compile.triton('triton_poi_fused_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=[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_4', '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_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 289) % 256
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wp/cwpw7dk62l5he4ut4o4mxis6ehd6a7vfphbozxk5xleqojv6xq6z.py
# Topologically Sorted Source Nodes: [max_pool2d_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_2 => _low_memory_max_pool2d_offsets_to_indices_2, _low_memory_max_pool2d_with_offsets_2, getitem_4
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_2 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%where_2, [2, 2], [2, 2], [1, 1], [1, 1], False), kwargs = {})
#   %getitem_4 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 0), kwargs = {})
#   %_low_memory_max_pool2d_offsets_to_indices_2 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_offsets_to_indices.default](args = (%getitem_5, 2, 17, [2, 2], [1, 1]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_5 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 9) % 9
    x0 = xindex % 9
    x2 = (xindex // 81)
    x4 = xindex
    tmp0 = (-1) + (2*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 17, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-18) + (2*x0) + (34*x1) + (289*x2)), tmp10 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-17) + (2*x0) + (34*x1) + (289*x2)), tmp16 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2*x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + ((-1) + (2*x0) + (34*x1) + (289*x2)), tmp23 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + ((2*x0) + (34*x1) + (289*x2)), tmp26 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + (x4), tmp28, xmask)
    tl.store(out_ptr2 + (x4), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7w/c7ww3vdepjlrxzlzidiplohg5cvtk7dl4ru6ibp4slb6ivb2vjrp.py
# Topologically Sorted Source Nodes: [x_6, out4], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   out4 => gt_3, mul_3, where_3
#   x_6 => convolution_3
# Graph fragment:
#   %convolution_3 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_8, %primals_9, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_3 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 0.2), kwargs = {})
#   %where_3 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_3), 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=[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_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 = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 81) % 512
    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 = 0.2
    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_4/inductor_cache/ut/cut4hxrrkqpnihmxn5hei6ilacmd2o3qy5lji3fh3rcda22riew2.py
# Topologically Sorted Source Nodes: [max_pool2d_3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_3 => _low_memory_max_pool2d_offsets_to_indices_3, _low_memory_max_pool2d_with_offsets_3, getitem_6
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_3 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%where_3, [2, 2], [2, 2], [1, 1], [1, 1], False), kwargs = {})
#   %getitem_6 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 0), kwargs = {})
#   %_low_memory_max_pool2d_offsets_to_indices_3 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_offsets_to_indices.default](args = (%getitem_7, 2, 9, [2, 2], [1, 1]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_7 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 51200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 5) % 5
    x0 = xindex % 5
    x2 = (xindex // 25)
    x4 = xindex
    tmp0 = (-1) + (2*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 9, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-10) + (2*x0) + (18*x1) + (81*x2)), tmp10, eviction_policy='evict_last', other=float("-inf"))
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-9) + (2*x0) + (18*x1) + (81*x2)), tmp16, eviction_policy='evict_last', other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2*x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + ((-1) + (2*x0) + (18*x1) + (81*x2)), tmp23, eviction_policy='evict_last', other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + ((2*x0) + (18*x1) + (81*x2)), tmp26, eviction_policy='evict_last', other=float("-inf"))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + (x4), tmp28, None)
    tl.store(out_ptr2 + (x4), tmp46, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/av/caveetqikgzznwkgfbidjyrvrypscuz5sfhzv4y7gekezn7enlo3.py
# Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_8 => convolution_4
#   x_9 => gt_4, mul_4, where_4
# Graph fragment:
#   %convolution_4 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_6, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_4 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_4, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_4, 0.2), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %convolution_4, %mul_4), kwargs = {})
triton_poi_fused_convolution_leaky_relu_8 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[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_8', '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_8(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 102400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 25) % 1024
    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 = 0.2
    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_4/inductor_cache/b7/cb75plcxmjn7steopvtgygttvot2pduchb3gr7skccikei657rrr.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_5
#   x_11 => gt_5, mul_5, where_5
# Graph fragment:
#   %convolution_5 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_4, %primals_12, %primals_13, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_5 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_5, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_5, 0.2), kwargs = {})
#   %where_5 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %convolution_5, %mul_5), 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 = 51200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 25) % 512
    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 = 0.2
    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_4/inductor_cache/l2/cl2b6gcsqmom3jjsibpx2h6kanl7tkmygx7wx3xw3extgrsazpd6.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%max_unpool2d, %where_3], 1), kwargs = {})
triton_poi_fused_cat_10 = async_compile.triton('triton_poi_fused_cat_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_10', '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_cat_10(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 331776
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 81) % 1024
    x0 = xindex % 81
    x2 = (xindex // 82944)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (81*x1) + (41472*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 1024, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (81*((-512) + x1)) + (41472*x2)), tmp6, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ak/caktluqhxsdwmmze4u42tpkdxnxptxab4hck6um5dmflf7kdczps.py
# Topologically Sorted Source Nodes: [x_13, x_14], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_13 => convolution_6
#   x_14 => gt_6, mul_6, where_6
# Graph fragment:
#   %convolution_6 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_14, %primals_15, [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_6, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_6, 0.2), kwargs = {})
#   %where_6 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %convolution_6, %mul_6), kwargs = {})
triton_poi_fused_convolution_leaky_relu_11 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_11', '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_11(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 81) % 256
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3l/c3ldpun2pjpr5izu4ji3yqakc65fcax6qejccfr4vhrdg32x2wdq.py
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%max_unpool2d_1, %where_2], 1), kwargs = {})
triton_poi_fused_cat_12 = async_compile.triton('triton_poi_fused_cat_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_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_cat_12(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 591872
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 289) % 512
    x0 = xindex % 289
    x2 = (xindex // 147968)
    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 + (289*x1) + (73984*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 512, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (289*((-256) + x1)) + (73984*x2)), tmp6, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/r5/cr54gf2noieft5ew2n4rdc36gxrljdof6umbsyk5g23pm66vfosh.py
# Topologically Sorted Source Nodes: [x_16, x_17], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_16 => convolution_7
#   x_17 => gt_7, mul_7, where_7
# Graph fragment:
#   %convolution_7 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_7 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_7, 0), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_7, 0.2), kwargs = {})
#   %where_7 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_7, %convolution_7, %mul_7), kwargs = {})
triton_poi_fused_convolution_leaky_relu_13 = async_compile.triton('triton_poi_fused_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=[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_13', '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_13(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 289) % 128
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nk/cnku3f55qyo2pmzirpmjpponqfowkicg6oyitqhul3qxdqlpplge.py
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%max_unpool2d_2, %where_1], 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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_14', '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_cat_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1115136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 1089) % 256
    x0 = xindex % 1089
    x2 = (xindex // 278784)
    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 + (1089*x1) + (139392*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 256, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (1089*((-128) + x1)) + (139392*x2)), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sq/csq6tgbaerl4v3spanlpmaladv637ehnm5dqgqyspncpcxlj4trd.py
# Topologically Sorted Source Nodes: [x_19, x_20], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_19 => convolution_8
#   x_20 => gt_8, mul_8, where_8
# Graph fragment:
#   %convolution_8 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_18, %primals_19, [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_8, 0), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_8, 0.2), kwargs = {})
#   %where_8 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_8, %convolution_8, %mul_8), 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=[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_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 = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 1089) % 64
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hx/chxv7q3z2ohnhirsw2xzjzriwuc5r5hmdwxvzgqa3qhx5ztu4olh.py
# Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_3 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%max_unpool2d_3, %where], 1), kwargs = {})
triton_poi_fused_cat_16 = async_compile.triton('triton_poi_fused_cat_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_16', '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_cat_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 128
    x0 = xindex % 4096
    x2 = (xindex // 524288)
    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 + (4096*x1) + (262144*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 128, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-64) + x1)) + (262144*x2)), tmp6, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/aj/cajtwj3wehhmespau3nwqid6gj3qpdvrtsqwu4rk2bcgxwjsvv7d.py
# Topologically Sorted Source Nodes: [x_22, x_23], Original ATen: [aten.convolution, aten._softmax]
# Source node to ATen node mapping:
#   x_22 => convolution_9
#   x_23 => amax, div, exp, sub, sum_1
# Graph fragment:
#   %convolution_9 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_20, %primals_21, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%convolution_9, [-3], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_9, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-3], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_convolution_17 = async_compile.triton('triton_poi_fused__softmax_convolution_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_convolution_17', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_convolution_17(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tmp3 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 / tmp5
    tl.store(in_out_ptr0 + (x0), tmp6, 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 = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (128, ), (1, ))
    assert_size_stride(primals_6, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_7, (256, ), (1, ))
    assert_size_stride(primals_8, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_9, (512, ), (1, ))
    assert_size_stride(primals_10, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_11, (1024, ), (1, ))
    assert_size_stride(primals_12, (512, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_13, (512, ), (1, ))
    assert_size_stride(primals_14, (256, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_15, (256, ), (1, ))
    assert_size_stride(primals_16, (128, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_17, (128, ), (1, ))
    assert_size_stride(primals_18, (64, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_19, (64, ), (1, ))
    assert_size_stride(primals_20, (1, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (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_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, out1], Original ATen: [aten.convolution, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0.run(buf0, primals_2, buf1, buf2, 1048576, grid=grid(1048576), stream=stream0)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((4, 64, 33, 33), (71680, 1120, 33, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1), torch.int64)
        # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf2, buf3, buf5, 278784, grid=grid(278784), stream=stream0)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 33, 33), (139392, 1089, 33, 1))
        buf7 = empty_strided_cuda((4, 128, 33, 33), (139392, 1089, 33, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 128, 33, 33), (139392, 1089, 33, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, out2], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf6, primals_5, buf7, buf8, 557568, grid=grid(557568), stream=stream0)
        del buf6
        del primals_5
        buf9 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1), torch.int64)
        # Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf8, buf9, buf11, 147968, grid=grid(147968), stream=stream0)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 17, 17), (73984, 289, 17, 1))
        buf13 = empty_strided_cuda((4, 256, 17, 17), (73984, 289, 17, 1), torch.bool)
        buf14 = empty_strided_cuda((4, 256, 17, 17), (73984, 289, 17, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4, out3], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf12, primals_7, buf13, buf14, 295936, grid=grid(295936), stream=stream0)
        del buf12
        del primals_7
        buf15 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.float32)
        buf17 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.int64)
        # Topologically Sorted Source Nodes: [max_pool2d_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_5.run(buf14, buf15, buf17, 82944, grid=grid(82944), stream=stream0)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf15, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 512, 9, 9), (41472, 81, 9, 1))
        buf19 = empty_strided_cuda((4, 512, 9, 9), (41472, 81, 9, 1), torch.bool)
        buf20 = empty_strided_cuda((4, 512, 9, 9), (41472, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, out4], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf18, primals_9, buf19, buf20, 165888, grid=grid(165888), stream=stream0)
        del buf18
        del primals_9
        buf21 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch.int64)
        # Topologically Sorted Source Nodes: [max_pool2d_3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_7.run(buf20, buf21, buf23, 51200, grid=grid(51200), stream=stream0)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf21, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 1024, 5, 5), (25600, 25, 5, 1))
        buf25 = empty_strided_cuda((4, 1024, 5, 5), (25600, 25, 5, 1), torch.bool)
        buf26 = empty_strided_cuda((4, 1024, 5, 5), (25600, 25, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_8.run(buf24, primals_11, buf25, buf26, 102400, grid=grid(102400), stream=stream0)
        del buf24
        del primals_11
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf27 = extern_kernels.convolution(buf26, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 512, 5, 5), (12800, 25, 5, 1))
        buf28 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch.bool)
        buf29 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_9.run(buf27, primals_13, buf28, buf29, 51200, grid=grid(51200), stream=stream0)
        del buf27
        del primals_13
        # Topologically Sorted Source Nodes: [x_10, x_11, x_12], Original ATen: [aten.convolution, aten.leaky_relu, aten.max_unpool2d]
        buf30 = torch.ops.aten.max_unpool2d.default(buf29, buf23, [9, 9])
        del buf29
        buf31 = buf30
        del buf30
        buf32 = empty_strided_cuda((4, 1024, 9, 9), (82944, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_10.run(buf31, buf20, buf32, 331776, grid=grid(331776), stream=stream0)
        del buf31
        # Topologically Sorted Source Nodes: [x_13], Original ATen: [aten.convolution]
        buf33 = extern_kernels.convolution(buf32, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 256, 9, 9), (20736, 81, 9, 1))
        buf34 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.bool)
        buf35 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_13, x_14], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_11.run(buf33, primals_15, buf34, buf35, 82944, grid=grid(82944), stream=stream0)
        del buf33
        del primals_15
        # Topologically Sorted Source Nodes: [x_13, x_14, x_15], Original ATen: [aten.convolution, aten.leaky_relu, aten.max_unpool2d]
        buf36 = torch.ops.aten.max_unpool2d.default(buf35, buf17, [17, 17])
        del buf35
        buf37 = buf36
        del buf36
        buf38 = empty_strided_cuda((4, 512, 17, 17), (147968, 289, 17, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_12.run(buf37, buf14, buf38, 591872, grid=grid(591872), stream=stream0)
        del buf37
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
        buf39 = extern_kernels.convolution(buf38, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 128, 17, 17), (36992, 289, 17, 1))
        buf40 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1), torch.bool)
        buf41 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_16, x_17], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_13.run(buf39, primals_17, buf40, buf41, 147968, grid=grid(147968), stream=stream0)
        del buf39
        del primals_17
        # Topologically Sorted Source Nodes: [x_16, x_17, x_18], Original ATen: [aten.convolution, aten.leaky_relu, aten.max_unpool2d]
        buf42 = torch.ops.aten.max_unpool2d.default(buf41, buf11, [33, 33])
        del buf41
        buf43 = buf42
        del buf42
        buf44 = empty_strided_cuda((4, 256, 33, 33), (278784, 1089, 33, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_14.run(buf43, buf8, buf44, 1115136, grid=grid(1115136), stream=stream0)
        del buf43
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.convolution]
        buf45 = extern_kernels.convolution(buf44, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf45, (4, 64, 33, 33), (69696, 1089, 33, 1))
        buf46 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1), torch.bool)
        buf47 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_19, x_20], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_15.run(buf45, primals_19, buf46, buf47, 278784, grid=grid(278784), stream=stream0)
        del buf45
        del primals_19
        # Topologically Sorted Source Nodes: [x_19, x_20, x_21], Original ATen: [aten.convolution, aten.leaky_relu, aten.max_unpool2d]
        buf48 = torch.ops.aten.max_unpool2d.default(buf47, buf5, [64, 64])
        del buf47
        buf49 = buf48
        del buf48
        buf50 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_16.run(buf49, buf2, buf50, 2097152, grid=grid(2097152), stream=stream0)
        del buf49
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf50, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf52 = buf51; del buf51  # reuse
        # Topologically Sorted Source Nodes: [x_22, x_23], Original ATen: [aten.convolution, aten._softmax]
        triton_poi_fused__softmax_convolution_17.run(buf52, primals_21, 16384, grid=grid(16384), stream=stream0)
        del primals_21
    return (buf52, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, buf1, buf2, buf3, buf5, buf7, buf8, buf9, buf11, buf13, buf14, buf15, buf17, buf19, buf20, buf21, buf23, buf25, buf26, buf28, buf32, buf34, buf38, buf40, buf44, buf46, buf50, buf52, )


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((64, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1024, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((512, 1024, 3, 3), (9216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((256, 1024, 3, 3), (9216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((128, 512, 3, 3), (4608, 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((64, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((1, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = 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])
    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 AE(nn.Module):

    def __init__(self):
        super(AE, self).__init__()
        self.leaky_reLU = nn.LeakyReLU(0.2)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=1,
            return_indices=True)
        self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2, padding=1)
        self.softmax = nn.Softmax2d()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=
            3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=128,
            kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(in_channels=128, out_channels=256,
            kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(in_channels=256, out_channels=512,
            kernel_size=3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(in_channels=512, out_channels=1024,
            kernel_size=3, stride=1, padding=1)
        self.conv6 = nn.Conv2d(in_channels=1024, out_channels=512,
            kernel_size=3, stride=1, padding=1)
        self.conv7 = nn.Conv2d(in_channels=1024, out_channels=256,
            kernel_size=3, stride=1, padding=1)
        self.conv8 = nn.Conv2d(in_channels=512, out_channels=128,
            kernel_size=3, stride=1, padding=1)
        self.conv9 = nn.Conv2d(in_channels=256, out_channels=64,
            kernel_size=3, stride=1, padding=1)
        self.conv10 = nn.Conv2d(in_channels=128, out_channels=1,
            kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = self.conv1(x)
        out1 = self.leaky_reLU(x)
        x = out1
        size1 = x.size()
        x, indices1 = self.pool(x)
        x = self.conv2(x)
        out2 = self.leaky_reLU(x)
        x = out2
        size2 = x.size()
        x, indices2 = self.pool(x)
        x = self.conv3(x)
        out3 = self.leaky_reLU(x)
        x = out3
        size3 = x.size()
        x, indices3 = self.pool(x)
        x = self.conv4(x)
        out4 = self.leaky_reLU(x)
        x = out4
        size4 = x.size()
        x, indices4 = self.pool(x)
        x = self.conv5(x)
        x = self.leaky_reLU(x)
        x = self.conv6(x)
        x = self.leaky_reLU(x)
        x = self.unpool(x, indices4, output_size=size4)
        x = self.conv7(torch.cat((x, out4), 1))
        x = self.leaky_reLU(x)
        x = self.unpool(x, indices3, output_size=size3)
        x = self.conv8(torch.cat((x, out3), 1))
        x = self.leaky_reLU(x)
        x = self.unpool(x, indices2, output_size=size2)
        x = self.conv9(torch.cat((x, out2), 1))
        x = self.leaky_reLU(x)
        x = self.unpool(x, indices1, output_size=size1)
        x = self.conv10(torch.cat((x, out1), 1))
        x = self.softmax(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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


@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 % 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 = 0.2
    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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 33 % 33
    x0 = xindex % 33
    x2 = xindex // 1089
    x4 = xindex % 1089
    x5 = xindex
    tmp0 = -1 + 2 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-65 + 2 * x0 + 128 * x1 + 4096 * x2), tmp10 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-64 + 2 * x0 + 128 * x1 + 4096 * x2), tmp16 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 128 * x1 + 4096 * x2), tmp23 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (2 * x0 + 128 * x1 + 4096 * x2), tmp26 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 
        0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + (x4 + 1120 * x2), tmp28, xmask)
    tl.store(out_ptr2 + x5, tmp46, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 1089 % 128
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 17 % 17
    x0 = xindex % 17
    x2 = xindex // 289
    x4 = xindex
    tmp0 = -1 + 2 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 33, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-34 + 2 * x0 + 66 * x1 + 1089 * x2), tmp10 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-33 + 2 * x0 + 66 * x1 + 1089 * x2), tmp16 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 66 * x1 + 1089 * x2), tmp23 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (2 * x0 + 66 * x1 + 1089 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=float('-inf'))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 
        0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + x4, tmp28, xmask)
    tl.store(out_ptr2 + x4, tmp46, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_4(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 289 % 256
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 9 % 9
    x0 = xindex % 9
    x2 = xindex // 81
    x4 = xindex
    tmp0 = -1 + 2 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 17, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-18 + 2 * x0 + 34 * x1 + 289 * x2), tmp10 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-17 + 2 * x0 + 34 * x1 + 289 * x2), tmp16 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 34 * x1 + 289 * x2), tmp23 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (2 * x0 + 34 * x1 + 289 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=float('-inf'))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 
        0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + x4, tmp28, xmask)
    tl.store(out_ptr2 + x4, tmp46, xmask)


@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 // 81 % 512
    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 = 0.2
    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_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x4 = xindex
    tmp0 = -1 + 2 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 9, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-10 + 2 * x0 + 18 * x1 + 81 * x2), tmp10,
        eviction_policy='evict_last', other=float('-inf'))
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-9 + 2 * x0 + 18 * x1 + 81 * x2), tmp16,
        eviction_policy='evict_last', other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 18 * x1 + 81 * x2), tmp23,
        eviction_policy='evict_last', other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (2 * x0 + 18 * x1 + 81 * x2), tmp26,
        eviction_policy='evict_last', other=float('-inf'))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tmp29 = tmp17 > tmp11
    tmp30 = tl.full([1], 1, tl.int8)
    tmp31 = tl.full([1], 0, tl.int8)
    tmp32 = tl.where(tmp29, tmp30, tmp31)
    tmp33 = tmp24 > tmp18
    tmp34 = tl.full([1], 2, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp27 > tmp25
    tmp37 = tl.full([1], 3, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tl.full([1], 2, tl.int32)
    tmp40 = tl.where((tmp38 < 0) != (tmp39 < 0), tl.where(tmp38 % tmp39 != 
        0, tmp38 // tmp39 - 1, tmp38 // tmp39), tmp38 // tmp39)
    tmp41 = tmp40 * tmp39
    tmp42 = tmp38 - tmp41
    tmp43 = tmp0 + tmp40
    tmp44 = tmp6 + tmp42
    tmp45 = tmp43 * tmp3
    tmp46 = tmp45 + tmp44
    tl.store(out_ptr0 + x4, tmp28, None)
    tl.store(out_ptr2 + x4, tmp46, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_8(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 // 25 % 1024
    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 = 0.2
    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_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 // 25 % 512
    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 = 0.2
    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_cat_10(in_ptr0, in_ptr1, 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 // 81 % 1024
    x0 = xindex % 81
    x2 = xindex // 82944
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 81 * x1 + 41472 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 1024, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 81 * (-512 + x1) + 41472 * x2), tmp6,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_11(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 256
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_cat_12(in_ptr0, in_ptr1, 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 // 289 % 512
    x0 = xindex % 289
    x2 = xindex // 147968
    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 + 289 * x1 + 73984 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 289 * (-256 + x1) + 73984 * x2), tmp6,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_13(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 289 % 128
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_cat_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1115136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 1089 % 256
    x0 = xindex % 1089
    x2 = xindex // 278784
    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 + 1089 * x1 + 139392 * x2), tmp4 & xmask,
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 1089 * (-128 + x1) + 139392 * x2), tmp6 &
        xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_15(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 1089 % 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_cat_16(in_ptr0, in_ptr1, 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 % 128
    x0 = xindex % 4096
    x2 = xindex // 524288
    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 + 4096 * x1 + 262144 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-64 + x1) + 262144 * x2), tmp6,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused__softmax_convolution_17(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tmp3 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 / tmp5
    tl.store(in_out_ptr0 + x0, tmp6, 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) = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_11, (1024,), (1,))
    assert_size_stride(primals_12, (512, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_13, (512,), (1,))
    assert_size_stride(primals_14, (256, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_15, (256,), (1,))
    assert_size_stride(primals_16, (128, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (64, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_19, (64,), (1,))
    assert_size_stride(primals_20, (1, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        buf2 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(1048576)](buf0,
            primals_2, buf1, buf2, 1048576, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((4, 64, 33, 33), (71680, 1120, 33, 1),
            torch.float32)
        buf5 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.int64)
        triton_poi_fused_max_pool2d_with_indices_1[grid(278784)](buf2, buf3,
            buf5, 278784, XBLOCK=512, num_warps=8, num_stages=1)
        buf6 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 33, 33), (139392, 1089, 33, 1))
        buf7 = empty_strided_cuda((4, 128, 33, 33), (139392, 1089, 33, 1),
            torch.bool)
        buf8 = empty_strided_cuda((4, 128, 33, 33), (139392, 1089, 33, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_2[grid(557568)](buf6,
            primals_5, buf7, buf8, 557568, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf6
        del primals_5
        buf9 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.float32)
        buf11 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.int64)
        triton_poi_fused_max_pool2d_with_indices_3[grid(147968)](buf8, buf9,
            buf11, 147968, XBLOCK=512, num_warps=8, num_stages=1)
        buf12 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 17, 17), (73984, 289, 17, 1))
        buf13 = empty_strided_cuda((4, 256, 17, 17), (73984, 289, 17, 1),
            torch.bool)
        buf14 = empty_strided_cuda((4, 256, 17, 17), (73984, 289, 17, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_4[grid(295936)](buf12,
            primals_7, buf13, buf14, 295936, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf12
        del primals_7
        buf15 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch
            .float32)
        buf17 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch
            .int64)
        triton_poi_fused_max_pool2d_with_indices_5[grid(82944)](buf14,
            buf15, buf17, 82944, XBLOCK=512, num_warps=8, num_stages=1)
        buf18 = extern_kernels.convolution(buf15, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 512, 9, 9), (41472, 81, 9, 1))
        buf19 = empty_strided_cuda((4, 512, 9, 9), (41472, 81, 9, 1), torch
            .bool)
        buf20 = empty_strided_cuda((4, 512, 9, 9), (41472, 81, 9, 1), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_6[grid(165888)](buf18,
            primals_9, buf19, buf20, 165888, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf18
        del primals_9
        buf21 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .float32)
        buf23 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .int64)
        triton_poi_fused_max_pool2d_with_indices_7[grid(51200)](buf20,
            buf21, buf23, 51200, XBLOCK=512, num_warps=4, num_stages=1)
        buf24 = extern_kernels.convolution(buf21, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 1024, 5, 5), (25600, 25, 5, 1))
        buf25 = empty_strided_cuda((4, 1024, 5, 5), (25600, 25, 5, 1),
            torch.bool)
        buf26 = empty_strided_cuda((4, 1024, 5, 5), (25600, 25, 5, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_8[grid(102400)](buf24,
            primals_11, buf25, buf26, 102400, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf24
        del primals_11
        buf27 = extern_kernels.convolution(buf26, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 512, 5, 5), (12800, 25, 5, 1))
        buf28 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .bool)
        buf29 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_9[grid(51200)](buf27,
            primals_13, buf28, buf29, 51200, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf27
        del primals_13
        buf30 = torch.ops.aten.max_unpool2d.default(buf29, buf23, [9, 9])
        del buf29
        buf31 = buf30
        del buf30
        buf32 = empty_strided_cuda((4, 1024, 9, 9), (82944, 81, 9, 1),
            torch.float32)
        triton_poi_fused_cat_10[grid(331776)](buf31, buf20, buf32, 331776,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del buf31
        buf33 = extern_kernels.convolution(buf32, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 256, 9, 9), (20736, 81, 9, 1))
        buf34 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch
            .bool)
        buf35 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_11[grid(82944)](buf33,
            primals_15, buf34, buf35, 82944, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf33
        del primals_15
        buf36 = torch.ops.aten.max_unpool2d.default(buf35, buf17, [17, 17])
        del buf35
        buf37 = buf36
        del buf36
        buf38 = empty_strided_cuda((4, 512, 17, 17), (147968, 289, 17, 1),
            torch.float32)
        triton_poi_fused_cat_12[grid(591872)](buf37, buf14, buf38, 591872,
            XBLOCK=512, num_warps=8, num_stages=1)
        del buf37
        buf39 = extern_kernels.convolution(buf38, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 128, 17, 17), (36992, 289, 17, 1))
        buf40 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.bool)
        buf41 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_13[grid(147968)](buf39,
            primals_17, buf40, buf41, 147968, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf39
        del primals_17
        buf42 = torch.ops.aten.max_unpool2d.default(buf41, buf11, [33, 33])
        del buf41
        buf43 = buf42
        del buf42
        buf44 = empty_strided_cuda((4, 256, 33, 33), (278784, 1089, 33, 1),
            torch.float32)
        triton_poi_fused_cat_14[grid(1115136)](buf43, buf8, buf44, 1115136,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del buf43
        buf45 = extern_kernels.convolution(buf44, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf45, (4, 64, 33, 33), (69696, 1089, 33, 1))
        buf46 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.bool)
        buf47 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_15[grid(278784)](buf45,
            primals_19, buf46, buf47, 278784, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf45
        del primals_19
        buf48 = torch.ops.aten.max_unpool2d.default(buf47, buf5, [64, 64])
        del buf47
        buf49 = buf48
        del buf48
        buf50 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_16[grid(2097152)](buf49, buf2, buf50, 2097152,
            XBLOCK=512, num_warps=8, num_stages=1)
        del buf49
        buf51 = extern_kernels.convolution(buf50, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf52 = buf51
        del buf51
        triton_poi_fused__softmax_convolution_17[grid(16384)](buf52,
            primals_21, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_21
    return (buf52, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, primals_18,
        primals_20, buf1, buf2, buf3, buf5, buf7, buf8, buf9, buf11, buf13,
        buf14, buf15, buf17, buf19, buf20, buf21, buf23, buf25, buf26,
        buf28, buf32, buf34, buf38, buf40, buf44, buf46, buf50, buf52)


class AENew(nn.Module):

    def __init__(self):
        super(AENew, self).__init__()
        self.leaky_reLU = nn.LeakyReLU(0.2)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=1,
            return_indices=True)
        self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2, padding=1)
        self.softmax = nn.Softmax2d()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=
            3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=128,
            kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(in_channels=128, out_channels=256,
            kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(in_channels=256, out_channels=512,
            kernel_size=3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(in_channels=512, out_channels=1024,
            kernel_size=3, stride=1, padding=1)
        self.conv6 = nn.Conv2d(in_channels=1024, out_channels=512,
            kernel_size=3, stride=1, padding=1)
        self.conv7 = nn.Conv2d(in_channels=1024, out_channels=256,
            kernel_size=3, stride=1, padding=1)
        self.conv8 = nn.Conv2d(in_channels=512, out_channels=128,
            kernel_size=3, stride=1, padding=1)
        self.conv9 = nn.Conv2d(in_channels=256, out_channels=64,
            kernel_size=3, stride=1, padding=1)
        self.conv10 = nn.Conv2d(in_channels=128, out_channels=1,
            kernel_size=3, stride=1, padding=1)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.bias
        primals_12 = self.conv6.weight
        primals_13 = self.conv6.bias
        primals_14 = self.conv7.weight
        primals_15 = self.conv7.bias
        primals_16 = self.conv8.weight
        primals_17 = self.conv8.bias
        primals_18 = self.conv9.weight
        primals_19 = self.conv9.bias
        primals_20 = self.conv10.weight
        primals_21 = self.conv10.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21])
        return output[0]
