# 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_1/inductor_cache/pl/cpl6jhqnafnew6pronlksao6s33bcslpzm3k66a74phcycboxpwr.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[4096, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4096
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (1024*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/eb/cebfmp3xsydvhof2vuiuzrtwr7fwapeufpm52glmntqds2lsygkv.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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=[4096, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4096
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/q4/cq46xznhsxlsfekongioaargva4mzxnrgtlpbrrnhugzm5zrigaf.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_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=[8192, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8192
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (1024*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/j3/cj3fmdhhayqju7oi6cghha6ztxe3k6a4nkplf3ytoh6ngdwwzu7z.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [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_3 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256, 1024], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 256
    xnumel = 1024
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (1024*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, 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 + (y0 + (64*x2) + (65536*y1)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (64*x2) + (65536*y1)), tmp7, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/iz/ciztmvwhv6emnancfpy7cbrz6fbor5fknq7e6dfwrlphuy4zy66p.py
# Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_2 => convolution_1
#   x_3 => gt_1, mul_1, where_1
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [2, 2], [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_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), 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 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/b5/cb5iajevrdo3fbl5mipzyzddlpduxqhwtlbdflw2gup5i3z247kz.py
# Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_4 => convolution_2
#   x_5 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_6, %primals_7, [2, 2], [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_5 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), 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 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/dr/cdr7wjqsdk2p5ntjftsxepatlnakzdou7akohpoxs4254rwwy7uh.py
# Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_6 => convolution_3
#   x_7 => gt_3, mul_3, where_3
# Graph fragment:
#   %convolution_3 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_8, %primals_9, [2, 2], [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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), 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 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ws/cwsloz2nozeca4pbc5td32dg2nwfiwwaawcofnu4kllep4dyfibq.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 = (%where_3, %primals_10, %primals_11, [2, 2], [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=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %convolution_4, %mul_4), kwargs = {})
triton_poi_fused_convolution_leaky_relu_7 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_7(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), 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 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/by/cbyuoo67qj25di4wzzzqcnljridqmsdvfw6yv42kpcdkt3j5tijh.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, [2, 2], [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=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %convolution_5, %mul_5), 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), 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 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ky/cky2wewewh75fidvmgc75rn34mqutgqo72olv7c7ywm6safu7neu.py
# Topologically Sorted Source Nodes: [d], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   d => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu, %where_5], 1), kwargs = {})
triton_poi_fused_cat_9 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 128
    x1 = (xindex // 128)
    x2 = xindex
    tmp0 = x0
    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 + ((64*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 128, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/mr/cmrxn2w2kzg4xucbro2b2yyj7kcomwo6vowwdmcnzrtztn63ir4r.py
# Topologically Sorted Source Nodes: [d_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   d_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_1, %where_4], 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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128)
    x2 = xindex
    tmp0 = x0
    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 + ((64*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 128, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ak/cakh7qvd4rmaebq6nb366mnbyaq4mnjen5rug3lqlxfnckkpranh.py
# Topologically Sorted Source Nodes: [d_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   d_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_2, %where_3], 1), kwargs = {})
triton_poi_fused_cat_11 = async_compile.triton('triton_poi_fused_cat_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_11(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128)
    x2 = xindex
    tmp0 = x0
    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 + ((64*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 128, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/wj/cwj6yqdhzukngdx76z3e47h7tsxa5fhdk5no73qs5teskgq7wfec.py
# Topologically Sorted Source Nodes: [d_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   d_3 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_3, %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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128)
    x2 = xindex
    tmp0 = x0
    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 + ((64*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 128, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ol/colnjxajhneas5qty6eu7cprkaqwiwz3s7bfjwhr3cxvpqoglxj7.py
# Topologically Sorted Source Nodes: [d_4], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   d_4 => cat_4
# Graph fragment:
#   %cat_4 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_4, %where_1], 1), kwargs = {})
triton_poi_fused_cat_13 = async_compile.triton('triton_poi_fused_cat_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_13(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128)
    x2 = xindex
    tmp0 = x0
    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 + ((64*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 128, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/o4/co42hdvcxdbdwalpuyppjhd2yhgle4abuqguymvzibhnbk6fsjnt.py
# Topologically Sorted Source Nodes: [d_5], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   d_5 => cat_5
# Graph fragment:
#   %cat_5 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_5, %where], 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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_14(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128)
    x2 = xindex
    tmp0 = x0
    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 + ((64*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 128, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/kn/ckndmhylafj2xjjbojaldf3kvsx6yrfcydzry3bpi4vgys5rzocj.py
# Topologically Sorted Source Nodes: [x_29, x_30], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_29 => convolution_13
#   x_30 => relu_6
# Graph fragment:
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_5, %primals_28, %primals_29, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_13,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_15', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_15(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    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 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + (x0), tmp5, None)
    tl.store(out_ptr0 + (x0), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/qz/cqzs6uatjxsyi5rzxclwzggmrfa73yocsguoqutckcw57afpanm7.py
# Topologically Sorted Source Nodes: [x_27, x_28], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_27 => convolution_12
#   x_28 => relu_5
# Graph fragment:
#   %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_4, %primals_26, %primals_27, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_5, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/mz/cmzim3gw2243r7f2ygq4swceufpovbbk4ueefpzrzrlu5urpsm6g.py
# Topologically Sorted Source Nodes: [x_25, x_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_25 => convolution_11
#   x_26 => relu_4
# Graph fragment:
#   %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_24, %primals_25, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_17 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/rd/crdw4ivtgbr3sclm6qgr5d6vbt5wrxvpjncbynksybiipsuuk3jl.py
# Topologically Sorted Source Nodes: [x_23, x_24], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_23 => convolution_10
#   x_24 => relu_3
# Graph fragment:
#   %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_22, %primals_23, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_18 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/xx/cxxulyqvngs25yt4sgkzdlp7qeq66rdujb277tfr26ok46jatda2.py
# Topologically Sorted Source Nodes: [x_20, x_22], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_20 => convolution_9
#   x_22 => relu_2
# Graph fragment:
#   %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_20, %primals_21, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_19 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_19', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_19(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/f6/cf6spwua6bfx4qoztvryiqg7w36exesl5wohnwhh6kqcd2vh63ci.py
# Topologically Sorted Source Nodes: [x_17, x_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_17 => convolution_8
#   x_19 => relu_1
# Graph fragment:
#   %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_18, %primals_19, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {})
#   %le_5 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_20 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_20', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_20(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/a3/ca3gocijxcch6odqpvh3znou4g5x6uzflpnr5fewwrooqeusqqkb.py
# Topologically Sorted Source Nodes: [x_14, x_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_14 => convolution_7
#   x_16 => relu
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_6, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
#   %le_6 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_21 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_21', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29 = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_11, (64, ), (1, ))
    assert_size_stride(primals_12, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_13, (64, ), (1, ))
    assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (64, ), (1, ))
    assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_17, (64, ), (1, ))
    assert_size_stride(primals_18, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_19, (64, ), (1, ))
    assert_size_stride(primals_20, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_21, (64, ), (1, ))
    assert_size_stride(primals_22, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_23, (64, ), (1, ))
    assert_size_stride(primals_24, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_25, (64, ), (1, ))
    assert_size_stride(primals_26, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_27, (64, ), (1, ))
    assert_size_stride(primals_28, (128, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_29, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_4, buf0, 4096, 16, grid=grid(4096, 16), stream=stream0)
        del primals_4
        buf1 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_0.run(primals_6, buf1, 4096, 16, grid=grid(4096, 16), stream=stream0)
        del primals_6
        buf2 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_0.run(primals_8, buf2, 4096, 16, grid=grid(4096, 16), stream=stream0)
        del primals_8
        buf3 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_0.run(primals_10, buf3, 4096, 16, grid=grid(4096, 16), stream=stream0)
        del primals_10
        buf4 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_0.run(primals_12, buf4, 4096, 16, grid=grid(4096, 16), stream=stream0)
        del primals_12
        buf5 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_14, buf5, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_14
        buf6 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_16, buf6, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_16
        buf7 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_18, buf7, 8192, 16, grid=grid(8192, 16), stream=stream0)
        del primals_18
        buf8 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_20, buf8, 8192, 16, grid=grid(8192, 16), stream=stream0)
        del primals_20
        buf9 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_22, buf9, 8192, 16, grid=grid(8192, 16), stream=stream0)
        del primals_22
        buf10 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_24, buf10, 8192, 16, grid=grid(8192, 16), stream=stream0)
        del primals_24
        buf11 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_26, buf11, 8192, 16, grid=grid(8192, 16), stream=stream0)
        del primals_26
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf13 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.bool)
        buf14 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_3.run(buf12, primals_2, buf13, buf14, 256, 1024, grid=grid(256, 1024), stream=stream0)
        del buf12
        del primals_2
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, buf0, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf16 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64), torch.bool)
        buf17 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf15, primals_5, buf16, buf17, 65536, grid=grid(65536), stream=stream0)
        del buf15
        del primals_5
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, buf1, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 8, 8), (4096, 1, 512, 64))
        buf19 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch.bool)
        buf20 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch.float32)
        # Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_5.run(buf18, primals_7, buf19, buf20, 16384, grid=grid(16384), stream=stream0)
        del buf18
        del primals_7
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf21 = extern_kernels.convolution(buf20, buf2, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf22 = empty_strided_cuda((4, 64, 4, 4), (1024, 1, 256, 64), torch.bool)
        buf23 = empty_strided_cuda((4, 64, 4, 4), (1024, 1, 256, 64), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf21, primals_9, buf22, buf23, 4096, grid=grid(4096), stream=stream0)
        del buf21
        del primals_9
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, buf3, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 64, 2, 2), (256, 1, 128, 64))
        buf25 = empty_strided_cuda((4, 64, 2, 2), (256, 1, 128, 64), torch.bool)
        buf26 = empty_strided_cuda((4, 64, 2, 2), (256, 1, 128, 64), torch.float32)
        # Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_7.run(buf24, primals_11, buf25, buf26, 1024, grid=grid(1024), stream=stream0)
        del buf24
        del primals_11
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf27 = extern_kernels.convolution(buf26, buf4, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 64, 1, 1), (64, 1, 64, 64))
        buf28 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.bool)
        buf29 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.float32)
        # Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_8.run(buf27, primals_13, buf28, buf29, 256, grid=grid(256), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 64, 1, 1), (64, 1, 64, 64))
        buf31 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.bool)
        buf32 = buf27; del buf27  # reuse
        # Topologically Sorted Source Nodes: [x_12, x_13], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_8.run(buf30, primals_15, buf31, buf32, 256, grid=grid(256), stream=stream0)
        del buf30
        del primals_15
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.convolution]
        buf33 = extern_kernels.convolution(buf32, buf6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 64, 1, 1), (64, 1, 64, 64))
        buf34 = empty_strided_cuda((4, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        # Topologically Sorted Source Nodes: [d], Original ATen: [aten.cat]
        triton_poi_fused_cat_9.run(buf33, primals_17, buf29, buf34, 512, grid=grid(512), stream=stream0)
        # Topologically Sorted Source Nodes: [x_17], Original ATen: [aten.convolution]
        buf35 = extern_kernels.convolution(buf34, buf7, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 64, 2, 2), (256, 1, 128, 64))
        buf36 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [d_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_10.run(buf35, primals_19, buf26, buf36, 2048, grid=grid(2048), stream=stream0)
        # Topologically Sorted Source Nodes: [x_20], Original ATen: [aten.convolution]
        buf37 = extern_kernels.convolution(buf36, buf8, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf37, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf38 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128), torch.float32)
        # Topologically Sorted Source Nodes: [d_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_11.run(buf37, primals_21, buf23, buf38, 8192, grid=grid(8192), stream=stream0)
        # Topologically Sorted Source Nodes: [x_23], Original ATen: [aten.convolution]
        buf39 = extern_kernels.convolution(buf38, buf9, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 64, 8, 8), (4096, 1, 512, 64))
        buf40 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128), torch.float32)
        # Topologically Sorted Source Nodes: [d_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_12.run(buf39, primals_23, buf20, buf40, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.convolution]
        buf41 = extern_kernels.convolution(buf40, buf10, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf41, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf42 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.float32)
        # Topologically Sorted Source Nodes: [d_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_13.run(buf41, primals_25, buf17, buf42, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [x_27], Original ATen: [aten.convolution]
        buf43 = extern_kernels.convolution(buf42, buf11, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 64, 32, 32), (65536, 1, 2048, 64))
        buf44 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.float32)
        # Topologically Sorted Source Nodes: [d_5], Original ATen: [aten.cat]
        triton_poi_fused_cat_14.run(buf43, primals_27, buf14, buf44, 524288, grid=grid(524288), stream=stream0)
        # Topologically Sorted Source Nodes: [x_29], Original ATen: [aten.convolution]
        buf45 = extern_kernels.convolution(buf44, primals_28, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf45, (4, 1, 64, 64), (4096, 1, 64, 1))
        buf46 = reinterpret_tensor(buf45, (4, 1, 64, 64), (4096, 4096, 64, 1), 0); del buf45  # reuse
        buf47 = empty_strided_cuda((4, 1, 64, 64), (4096, 1, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_29, x_30], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_15.run(buf46, primals_29, buf47, 16384, grid=grid(16384), stream=stream0)
        del primals_29
        buf48 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.bool)
        # Topologically Sorted Source Nodes: [x_27, x_28], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_16.run(buf43, primals_27, buf48, 262144, grid=grid(262144), stream=stream0)
        del buf43
        del primals_27
        buf49 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64), torch.bool)
        # Topologically Sorted Source Nodes: [x_25, x_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_17.run(buf41, primals_25, buf49, 65536, grid=grid(65536), stream=stream0)
        del buf41
        del primals_25
        buf50 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch.bool)
        # Topologically Sorted Source Nodes: [x_23, x_24], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_18.run(buf39, primals_23, buf50, 16384, grid=grid(16384), stream=stream0)
        del buf39
        del primals_23
        buf51 = empty_strided_cuda((4, 64, 4, 4), (1024, 1, 256, 64), torch.bool)
        # Topologically Sorted Source Nodes: [x_20, x_22], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_19.run(buf37, primals_21, buf51, 4096, grid=grid(4096), stream=stream0)
        del buf37
        del primals_21
        buf52 = empty_strided_cuda((4, 64, 2, 2), (256, 1, 128, 64), torch.bool)
        # Topologically Sorted Source Nodes: [x_17, x_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_20.run(buf35, primals_19, buf52, 1024, grid=grid(1024), stream=stream0)
        del buf35
        del primals_19
        buf53 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.bool)
        # Topologically Sorted Source Nodes: [x_14, x_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_21.run(buf33, primals_17, buf53, 256, grid=grid(256), stream=stream0)
        del buf33
        del primals_17
    return (buf46, primals_1, primals_3, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, buf11, primals_28, buf13, buf14, buf16, buf17, buf19, buf20, buf22, buf23, buf25, buf26, buf28, buf29, buf31, buf32, buf34, buf36, buf38, buf40, buf42, buf44, buf47, buf48, buf49, buf50, buf51, buf52, buf53, )


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, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((128, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29])
    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 Conv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bn
        =False, activation='leakyrelu', dropout=False):
        super(Conv2d, self).__init__()
        padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding=padding)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0,
            affine=True) if bn else None
        self.dropout = nn.Dropout(p=0.5) if dropout else None
        if activation == 'leakyrelu':
            self.activation = nn.LeakyReLU(negative_slope=0.2)
        elif activation == 'relu':
            self.activation = nn.ReLU()
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        else:
            raise ValueError('Not a valid activation, received {}'.format(
                activation))

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.dropout is not None:
            x = self.dropout(x)
        x = self.activation(x)
        return x


class Deconv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bn
        =False, activation='leakyrelu', dropout=False):
        super(Deconv2d, self).__init__()
        padding = int((kernel_size - 1) / 2)
        self.conv = nn.ConvTranspose2d(in_channels, out_channels,
            kernel_size, stride, padding=padding)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0,
            affine=True) if bn else None
        self.dropout = nn.Dropout(p=0.5) if dropout else None
        if activation == 'leakyrelu':
            self.activation = nn.LeakyReLU(negative_slope=0.2)
        elif activation == 'relu':
            self.activation = nn.ReLU()
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        else:
            raise ValueError('Not a valid activation, received {}'.format(
                activation))

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.dropout is not None:
            x = self.dropout(x)
        x = self.activation(x)
        return x


class G_Small(nn.Module):

    def __init__(self):
        super(G_Small, self).__init__()
        self.encoder_1 = Conv2d(1, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_2 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_3 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_4 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_5 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_6 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_7 = Conv2d(64, 64, 3, stride=1, bn=False, activation=
            'leakyrelu', dropout=False)
        self.decoder_1 = Deconv2d(64, 64, 3, stride=1, bn=False, activation
            ='relu', dropout=True)
        self.decoder_2 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=True)
        self.decoder_3 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=True)
        self.decoder_4 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=False)
        self.decoder_5 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=False)
        self.decoder_6 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=False)
        self.decoder_7 = Deconv2d(128, 1, 4, stride=2, bn=False, activation
            ='relu', dropout=False)

    def forward(self, x):
        e1 = self.encoder_1(x)
        e2 = self.encoder_2(e1)
        e3 = self.encoder_3(e2)
        e4 = self.encoder_4(e3)
        e5 = self.encoder_5(e4)
        e6 = self.encoder_6(e5)
        e7 = self.encoder_7(e6)
        d = self.decoder_1(e7)
        d = torch.cat((d, e6), dim=1)
        d = self.decoder_2(d)
        d = torch.cat((d, e5), dim=1)
        d = self.decoder_3(d)
        d = torch.cat((d, e4), dim=1)
        d = self.decoder_4(d)
        d = torch.cat((d, e3), dim=1)
        d = self.decoder_5(d)
        d = torch.cat((d, e2), dim=1)
        d = self.decoder_6(d)
        d = torch.cat((d, e1), dim=1)
        d = self.decoder_7(d)
        return d


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 1024 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 1024 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 1024
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 1024 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, 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 + (y0 + 64 * x2 + 65536 * y1), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 64 * x2 + 65536 * y1), tmp7, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_4(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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, 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 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_5(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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, 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 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_6(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, 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 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_7(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, 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 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_8(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, 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 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_cat_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 128
    x1 = xindex // 128
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_cat_10(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_cat_11(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_cat_12(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_cat_13(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_cat_14(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_15(in_out_ptr0,
    in_ptr0, out_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 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + x0, tmp5, None)
    tl.store(out_ptr0 + x0, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_17(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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_18(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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_19(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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_20(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29) = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_17, (64,), (1,))
    assert_size_stride(primals_18, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_19, (64,), (1,))
    assert_size_stride(primals_20, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_21, (64,), (1,))
    assert_size_stride(primals_22, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_23, (64,), (1,))
    assert_size_stride(primals_24, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_25, (64,), (1,))
    assert_size_stride(primals_26, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_27, (64,), (1,))
    assert_size_stride(primals_28, (128, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_29, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(4096, 16)](primals_4, buf0, 4096, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch
            .float32)
        triton_poi_fused_0[grid(4096, 16)](primals_6, buf1, 4096, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf2 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch
            .float32)
        triton_poi_fused_0[grid(4096, 16)](primals_8, buf2, 4096, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf3 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch
            .float32)
        triton_poi_fused_0[grid(4096, 16)](primals_10, buf3, 4096, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_10
        buf4 = empty_strided_cuda((64, 64, 4, 4), (1024, 1, 256, 64), torch
            .float32)
        triton_poi_fused_0[grid(4096, 16)](primals_12, buf4, 4096, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_12
        buf5 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_14, buf5, 4096, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_14
        buf6 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_16, buf6, 4096, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_16
        buf7 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64),
            torch.float32)
        triton_poi_fused_2[grid(8192, 16)](primals_18, buf7, 8192, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_18
        buf8 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64),
            torch.float32)
        triton_poi_fused_2[grid(8192, 16)](primals_20, buf8, 8192, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_20
        buf9 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64),
            torch.float32)
        triton_poi_fused_2[grid(8192, 16)](primals_22, buf9, 8192, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_22
        buf10 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64),
            torch.float32)
        triton_poi_fused_2[grid(8192, 16)](primals_24, buf10, 8192, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_24
        buf11 = empty_strided_cuda((128, 64, 4, 4), (1024, 1, 256, 64),
            torch.float32)
        triton_poi_fused_2[grid(8192, 16)](primals_26, buf11, 8192, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_26
        buf12 = extern_kernels.convolution(primals_3, primals_1, stride=(2,
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf13 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
            torch.bool)
        buf14 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_3[grid(256, 1024)](buf12,
            primals_2, buf13, buf14, 256, 1024, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del buf12
        del primals_2
        buf15 = extern_kernels.convolution(buf14, buf0, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf16 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64),
            torch.bool)
        buf17 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_4[grid(65536)](buf15,
            primals_5, buf16, buf17, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf15
        del primals_5
        buf18 = extern_kernels.convolution(buf17, buf1, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 8, 8), (4096, 1, 512, 64))
        buf19 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch
            .bool)
        buf20 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_5[grid(16384)](buf18,
            primals_7, buf19, buf20, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf18
        del primals_7
        buf21 = extern_kernels.convolution(buf20, buf2, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf22 = empty_strided_cuda((4, 64, 4, 4), (1024, 1, 256, 64), torch
            .bool)
        buf23 = empty_strided_cuda((4, 64, 4, 4), (1024, 1, 256, 64), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_6[grid(4096)](buf21,
            primals_9, buf22, buf23, 4096, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf21
        del primals_9
        buf24 = extern_kernels.convolution(buf23, buf3, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 64, 2, 2), (256, 1, 128, 64))
        buf25 = empty_strided_cuda((4, 64, 2, 2), (256, 1, 128, 64), torch.bool
            )
        buf26 = empty_strided_cuda((4, 64, 2, 2), (256, 1, 128, 64), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_7[grid(1024)](buf24,
            primals_11, buf25, buf26, 1024, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf24
        del primals_11
        buf27 = extern_kernels.convolution(buf26, buf4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 64, 1, 1), (64, 1, 64, 64))
        buf28 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.bool)
        buf29 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_8[grid(256)](buf27,
            primals_13, buf28, buf29, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_13
        buf30 = extern_kernels.convolution(buf29, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 64, 1, 1), (64, 1, 64, 64))
        buf31 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.bool)
        buf32 = buf27
        del buf27
        triton_poi_fused_convolution_leaky_relu_8[grid(256)](buf30,
            primals_15, buf31, buf32, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf30
        del primals_15
        buf33 = extern_kernels.convolution(buf32, buf6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 64, 1, 1), (64, 1, 64, 64))
        buf34 = empty_strided_cuda((4, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        triton_poi_fused_cat_9[grid(512)](buf33, primals_17, buf29, buf34, 
            512, XBLOCK=128, num_warps=4, num_stages=1)
        buf35 = extern_kernels.convolution(buf34, buf7, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 64, 2, 2), (256, 1, 128, 64))
        buf36 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_cat_10[grid(2048)](buf35, primals_19, buf26, buf36,
            2048, XBLOCK=256, num_warps=4, num_stages=1)
        buf37 = extern_kernels.convolution(buf36, buf8, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf37, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf38 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128),
            torch.float32)
        triton_poi_fused_cat_11[grid(8192)](buf37, primals_21, buf23, buf38,
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        buf39 = extern_kernels.convolution(buf38, buf9, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 64, 8, 8), (4096, 1, 512, 64))
        buf40 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128),
            torch.float32)
        triton_poi_fused_cat_12[grid(32768)](buf39, primals_23, buf20,
            buf40, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        buf41 = extern_kernels.convolution(buf40, buf10, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf41, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf42 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128),
            torch.float32)
        triton_poi_fused_cat_13[grid(131072)](buf41, primals_25, buf17,
            buf42, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        buf43 = extern_kernels.convolution(buf42, buf11, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 64, 32, 32), (65536, 1, 2048, 64))
        buf44 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128),
            torch.float32)
        triton_poi_fused_cat_14[grid(524288)](buf43, primals_27, buf14,
            buf44, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        buf45 = extern_kernels.convolution(buf44, primals_28, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf45, (4, 1, 64, 64), (4096, 1, 64, 1))
        buf46 = reinterpret_tensor(buf45, (4, 1, 64, 64), (4096, 4096, 64, 
            1), 0)
        del buf45
        buf47 = empty_strided_cuda((4, 1, 64, 64), (4096, 1, 64, 1), torch.bool
            )
        triton_poi_fused_convolution_relu_threshold_backward_15[grid(16384)](
            buf46, primals_29, buf47, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_29
        buf48 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_16[grid(262144)](
            buf43, primals_27, buf48, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf43
        del primals_27
        buf49 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_17[grid(65536)](
            buf41, primals_25, buf49, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf41
        del primals_25
        buf50 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch
            .bool)
        triton_poi_fused_convolution_relu_threshold_backward_18[grid(16384)](
            buf39, primals_23, buf50, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf39
        del primals_23
        buf51 = empty_strided_cuda((4, 64, 4, 4), (1024, 1, 256, 64), torch
            .bool)
        triton_poi_fused_convolution_relu_threshold_backward_19[grid(4096)](
            buf37, primals_21, buf51, 4096, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf37
        del primals_21
        buf52 = empty_strided_cuda((4, 64, 2, 2), (256, 1, 128, 64), torch.bool
            )
        triton_poi_fused_convolution_relu_threshold_backward_20[grid(1024)](
            buf35, primals_19, buf52, 1024, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf35
        del primals_19
        buf53 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 64, 64), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_21[grid(256)](
            buf33, primals_17, buf53, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf33
        del primals_17
    return (buf46, primals_1, primals_3, buf0, buf1, buf2, buf3, buf4, buf5,
        buf6, buf7, buf8, buf9, buf10, buf11, primals_28, buf13, buf14,
        buf16, buf17, buf19, buf20, buf22, buf23, buf25, buf26, buf28,
        buf29, buf31, buf32, buf34, buf36, buf38, buf40, buf42, buf44,
        buf47, buf48, buf49, buf50, buf51, buf52, buf53)


class Conv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bn
        =False, activation='leakyrelu', dropout=False):
        super(Conv2d, self).__init__()
        padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding=padding)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0,
            affine=True) if bn else None
        self.dropout = nn.Dropout(p=0.5) if dropout else None
        if activation == 'leakyrelu':
            self.activation = nn.LeakyReLU(negative_slope=0.2)
        elif activation == 'relu':
            self.activation = nn.ReLU()
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        else:
            raise ValueError('Not a valid activation, received {}'.format(
                activation))

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.dropout is not None:
            x = self.dropout(x)
        x = self.activation(x)
        return x


class Deconv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bn
        =False, activation='leakyrelu', dropout=False):
        super(Deconv2d, self).__init__()
        padding = int((kernel_size - 1) / 2)
        self.conv = nn.ConvTranspose2d(in_channels, out_channels,
            kernel_size, stride, padding=padding)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0,
            affine=True) if bn else None
        self.dropout = nn.Dropout(p=0.5) if dropout else None
        if activation == 'leakyrelu':
            self.activation = nn.LeakyReLU(negative_slope=0.2)
        elif activation == 'relu':
            self.activation = nn.ReLU()
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        else:
            raise ValueError('Not a valid activation, received {}'.format(
                activation))

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.dropout is not None:
            x = self.dropout(x)
        x = self.activation(x)
        return x


class G_SmallNew(nn.Module):

    def __init__(self):
        super(G_SmallNew, self).__init__()
        self.encoder_1 = Conv2d(1, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_2 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_3 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_4 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_5 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_6 = Conv2d(64, 64, 4, stride=2, bn=False, activation=
            'leakyrelu', dropout=False)
        self.encoder_7 = Conv2d(64, 64, 3, stride=1, bn=False, activation=
            'leakyrelu', dropout=False)
        self.decoder_1 = Deconv2d(64, 64, 3, stride=1, bn=False, activation
            ='relu', dropout=True)
        self.decoder_2 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=True)
        self.decoder_3 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=True)
        self.decoder_4 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=False)
        self.decoder_5 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=False)
        self.decoder_6 = Deconv2d(128, 64, 4, stride=2, bn=False,
            activation='relu', dropout=False)
        self.decoder_7 = Deconv2d(128, 1, 4, stride=2, bn=False, activation
            ='relu', dropout=False)

    def forward(self, input_0):
        primals_1 = self.encoder_1.conv.weight
        primals_2 = self.encoder_1.conv.bias
        primals_4 = self.encoder_2.conv.weight
        primals_5 = self.encoder_2.conv.bias
        primals_6 = self.encoder_3.conv.weight
        primals_7 = self.encoder_3.conv.bias
        primals_8 = self.encoder_4.conv.weight
        primals_9 = self.encoder_4.conv.bias
        primals_10 = self.encoder_5.conv.weight
        primals_11 = self.encoder_5.conv.bias
        primals_12 = self.encoder_6.conv.weight
        primals_13 = self.encoder_6.conv.bias
        primals_14 = self.encoder_7.conv.weight
        primals_15 = self.encoder_7.conv.bias
        primals_16 = self.decoder_1.conv.weight
        primals_17 = self.decoder_1.conv.bias
        primals_18 = self.decoder_2.conv.weight
        primals_19 = self.decoder_2.conv.bias
        primals_20 = self.decoder_3.conv.weight
        primals_21 = self.decoder_3.conv.bias
        primals_22 = self.decoder_4.conv.weight
        primals_23 = self.decoder_4.conv.bias
        primals_24 = self.decoder_5.conv.weight
        primals_25 = self.decoder_5.conv.bias
        primals_26 = self.decoder_6.conv.weight
        primals_27 = self.decoder_6.conv.bias
        primals_28 = self.decoder_7.conv.weight
        primals_29 = self.decoder_7.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29])
        return output[0]
