# 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_6/inductor_cache/7o/c7o5ttyweofz42kjophypjktbctv6rlvcw3xdxc4lcl7kzkau6rb.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=[256, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 256
    xnumel = 4096
    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 = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (262144*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cg/ccgjbjra22jzcgrej4cregu4qnsuv4valsbpzazlnyx6d4zadmny.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=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_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_6/inductor_cache/ra/crarmf7s2qf36jg27hprl42qtwcxcnnoyrgzgevtstzj4qgsdzwl.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=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8192
    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_6/inductor_cache/dd/cddy2xs2uderg6rhu3vap3su355lmjpkrmadmh5gnbcfg2frfd5z.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_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=[16384, 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=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    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 % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ih/cihu7ohoiwwrblocurozhw6ihpzbq4oc43mseo4n6wd7ronp74tw.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_4 = async_compile.triton('triton_poi_fused_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=[32768, 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=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 32768
    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 % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ks/ckseolxcn4us7f7nstfb5fcnac3setezrwkzoer7icg2hkznld3f.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_5 = async_compile.triton('triton_poi_fused_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536, 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=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 65536
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(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 % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/gb/cgbgdvu6dbspyyhnsmpzfukazt2ppl6i64rxeg4eon6csfalrolz.py
# Topologically Sorted Source Nodes: [conv2d, prelu], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d => convolution
#   prelu => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = 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 = (%view, %convolution), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_6 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_6(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/mv/cmvnowfb3oy3pbt7flo5qjqnjh7k3o4nvj3j352gyt4xogtyiqhr.py
# Topologically Sorted Source Nodes: [conv2d_1, out, out_1], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   out => gt_1, mul_1, where_1
#   out_1 => add
# Graph fragment:
#   %convolution_1 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_5, %primals_6, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_1 : [num_users=1] = 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 = (%view_1, %convolution_1), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %where_1), kwargs = {})
triton_poi_fused__prelu_kernel_add_convolution_7 = async_compile.triton('triton_poi_fused__prelu_kernel_add_convolution_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_add_convolution_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_7(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), None)
    tmp6 = tl.load(in_ptr2 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = 0.0
    tmp5 = tmp2 > tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp5, tmp2, tmp8)
    tmp10 = tmp3 + tmp9
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(out_ptr0 + (x2), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7w/c7w73gmmkdsirnvywtc4ajcpxvvxqgvvm4brbqqcjmmdkokgpdim.py
# Topologically Sorted Source Nodes: [conv2d_2, out_2], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   out_2 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_2 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%add, %primals_8, %primals_9, [2, 2], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_2 : [num_users=1] = 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 = (%view_2, %convolution_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__prelu_kernel_convolution_8 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_8(in_out_ptr0, in_ptr0, in_ptr1, 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)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/zk/czkkxdgtlyqrqdvrseox24se7djiklw4ywwqg5deataobvbixncw.py
# Topologically Sorted Source Nodes: [conv2d_3, out_3, out_4], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   out_3 => gt_3, mul_3, where_3
#   out_4 => add_1
# Graph fragment:
#   %convolution_3 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_11, %primals_12, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_3 : [num_users=1] = 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 = (%view_3, %convolution_3), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_3), kwargs = {})
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_2, %where_3), kwargs = {})
triton_poi_fused__prelu_kernel_add_convolution_9 = async_compile.triton('triton_poi_fused__prelu_kernel_add_convolution_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_add_convolution_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_9(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), None)
    tmp6 = tl.load(in_ptr2 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = 0.0
    tmp5 = tmp2 > tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp5, tmp2, tmp8)
    tmp10 = tmp3 + tmp9
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(out_ptr0 + (x2), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/fi/cfix2abznyuwifr2phemvjcx6o7zb25abcp4m24ipozb6rcuhaft.py
# Topologically Sorted Source Nodes: [conv2d_4, out_5], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   out_5 => gt_4, mul_4, where_4
# Graph fragment:
#   %convolution_4 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%add_1, %primals_14, %primals_15, [2, 2], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_4 : [num_users=1] = 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 = (%view_4, %convolution_4), 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__prelu_kernel_convolution_10 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_10(in_out_ptr0, 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 % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/uo/cuozfwqvuszxwhln4ecmax73rh2magjexf3mcj56cuthbp7saobb.py
# Topologically Sorted Source Nodes: [conv2d_5, out_6, out_7], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
# Source node to ATen node mapping:
#   conv2d_5 => convolution_5
#   out_6 => gt_5, mul_5, where_5
#   out_7 => add_2
# Graph fragment:
#   %convolution_5 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_4, %primals_17, %primals_18, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_5 : [num_users=1] = 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 = (%view_5, %convolution_5), kwargs = {})
#   %where_5 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %convolution_5, %mul_5), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_4, %where_5), kwargs = {})
triton_poi_fused__prelu_kernel_add_convolution_11 = async_compile.triton('triton_poi_fused__prelu_kernel_add_convolution_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_add_convolution_11', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_11(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), None)
    tmp6 = tl.load(in_ptr2 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = 0.0
    tmp5 = tmp2 > tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp5, tmp2, tmp8)
    tmp10 = tmp3 + tmp9
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(out_ptr0 + (x2), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/uz/cuzovxgz3xpshhaf3gtwzhdoklaeircgoc5xwk6ilmx6gxtmwsce.py
# Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_9 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%view_7, %add_1], 1), kwargs = {})
triton_poi_fused_cat_12 = async_compile.triton('triton_poi_fused_cat_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_12(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 256
    x1 = (xindex // 256) % 32
    x2 = (xindex // 8192) % 32
    x3 = (xindex // 262144)
    x4 = (xindex // 256)
    x5 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((2*(x2 % 2)) + (4*x0) + (512*(x1 // 2)) + (8192*(x2 // 2)) + (131072*x3) + (x1 % 2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + ((2*(x2 % 2)) + (4*x0) + (x1 % 2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 256, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + ((128*x4) + ((-128) + x0)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x5), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/le/clekkenw5icxsovzemdjklozrlbwhcapkmsbd6e5grknqd6xsyvy.py
# Topologically Sorted Source Nodes: [out_10], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out_10 => convolution_7
# Graph fragment:
#   %convolution_7 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_13 = async_compile.triton('triton_poi_fused_convolution_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/jo/cjoj4oqa3cykwpnoykynnq6m5n2tz6k6ogfuy7o5zmo6oogghuzw.py
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_14 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%view_10, %add], 1), kwargs = {})
triton_poi_fused_cat_14 = async_compile.triton('triton_poi_fused_cat_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_14(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    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) % 64
    x2 = (xindex // 8192) % 64
    x3 = (xindex // 524288)
    x4 = (xindex // 128)
    x5 = 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 + ((2*(x2 % 2)) + (4*x0) + (256*(x1 // 2)) + (8192*(x2 // 2)) + (262144*x3) + (x1 % 2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + ((2*(x2 % 2)) + (4*x0) + (x1 % 2)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 128, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + ((64*x4) + ((-64) + x0)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x5), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yy/cyyjsyxytcxq2twk3gzpswngag7dkwauaesis6dj3xgdhf2nzlxr.py
# Topologically Sorted Source Nodes: [out_15], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out_15 => convolution_10
# Graph fragment:
#   %convolution_10 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_29, %primals_30, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_15 = async_compile.triton('triton_poi_fused_convolution_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_15', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/oj/cojkkojtv4ngahkvad7zqurayjljjzpmcfzqr44legm2r2bdfyfb.py
# Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   out_18 => convolution_13
#   out_19 => add_5
# Graph fragment:
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_4, %primals_37, %primals_38, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_13, %primals_1), kwargs = {})
triton_poi_fused_add_convolution_16 = async_compile.triton('triton_poi_fused_add_convolution_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=[16384, 64], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_16(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 64
    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 % 4096
    y1 = (yindex // 4096)
    tmp0 = tl.load(in_ptr0 + (x2 + (64*y3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + (64*y3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (y0 + (4096*x2) + (262144*y1)), tmp4, 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, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38 = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 64, 64), (262144, 4096, 64, 1))
    assert_size_stride(primals_2, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_3, (64, ), (1, ))
    assert_size_stride(primals_4, (1, ), (1, ))
    assert_size_stride(primals_5, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_6, (64, ), (1, ))
    assert_size_stride(primals_7, (1, ), (1, ))
    assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (1, ), (1, ))
    assert_size_stride(primals_11, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_12, (128, ), (1, ))
    assert_size_stride(primals_13, (1, ), (1, ))
    assert_size_stride(primals_14, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (256, ), (1, ))
    assert_size_stride(primals_16, (1, ), (1, ))
    assert_size_stride(primals_17, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_18, (256, ), (1, ))
    assert_size_stride(primals_19, (1, ), (1, ))
    assert_size_stride(primals_20, (512, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_21, (512, ), (1, ))
    assert_size_stride(primals_22, (128, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_23, (128, ), (1, ))
    assert_size_stride(primals_24, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_25, (128, ), (1, ))
    assert_size_stride(primals_26, (1, ), (1, ))
    assert_size_stride(primals_27, (256, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_28, (256, ), (1, ))
    assert_size_stride(primals_29, (64, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_30, (64, ), (1, ))
    assert_size_stride(primals_31, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_32, (64, ), (1, ))
    assert_size_stride(primals_33, (1, ), (1, ))
    assert_size_stride(primals_34, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_35, (64, ), (1, ))
    assert_size_stride(primals_36, (1, ), (1, ))
    assert_size_stride(primals_37, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_38, (64, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 256, 4096, grid=grid(256, 4096), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_2, buf1, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_5, buf2, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_5
        buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_8, buf3, 8192, 9, grid=grid(8192, 9), stream=stream0)
        del primals_8
        buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_11, buf4, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_11
        buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_14, buf5, 32768, 9, grid=grid(32768, 9), stream=stream0)
        del primals_14
        buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_17, buf6, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_17
        buf7 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_24, buf7, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_24
        buf8 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_31, buf8, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_31
        buf9 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_34, buf9, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_34
        buf10 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_37, buf10, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf0, buf1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf12 = buf11; del buf11  # reuse
        buf13 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, prelu], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf12, primals_3, primals_4, buf13, 1048576, grid=grid(1048576), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf15 = buf14; del buf14  # reuse
        buf16 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_1, out, out_1], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
        triton_poi_fused__prelu_kernel_add_convolution_7.run(buf15, primals_6, buf0, primals_7, buf16, 1048576, grid=grid(1048576), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf16, buf3, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf18 = buf17; del buf17  # reuse
        buf19 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2, out_2], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_8.run(buf18, primals_9, primals_10, buf19, 524288, grid=grid(524288), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf21 = buf20; del buf20  # reuse
        buf22 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_3, out_3, out_4], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
        triton_poi_fused__prelu_kernel_add_convolution_9.run(buf21, primals_12, buf19, primals_13, buf22, 524288, grid=grid(524288), stream=stream0)
        del primals_12
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf23 = extern_kernels.convolution(buf22, buf5, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf23, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf24 = buf23; del buf23  # reuse
        buf25 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_4, out_5], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_10.run(buf24, primals_15, primals_16, buf25, 262144, grid=grid(262144), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, buf6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf27 = buf26; del buf26  # reuse
        buf28 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_5, out_6, out_7], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
        triton_poi_fused__prelu_kernel_add_convolution_11.run(buf27, primals_18, buf25, primals_19, buf28, 262144, grid=grid(262144), stream=stream0)
        del primals_18
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf29 = extern_kernels.convolution(buf28, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 512, 16, 16), (131072, 1, 8192, 512))
        buf30 = empty_strided_cuda((4, 256, 32, 32), (262144, 1, 8192, 256), torch.float32)
        # Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.cat]
        triton_poi_fused_cat_12.run(buf29, primals_21, buf22, buf30, 1048576, grid=grid(1048576), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [out_10], Original ATen: [aten.convolution]
        buf31 = extern_kernels.convolution(buf30, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf32 = buf31; del buf31  # reuse
        # Topologically Sorted Source Nodes: [out_10], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf32, primals_23, 524288, grid=grid(524288), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf33 = extern_kernels.convolution(buf32, buf7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf34 = buf33; del buf33  # reuse
        buf35 = reinterpret_tensor(buf29, (4, 128, 32, 32), (131072, 1, 4096, 128), 0); del buf29  # reuse
        # Topologically Sorted Source Nodes: [conv2d_8, out_11, out_12], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
        triton_poi_fused__prelu_kernel_add_convolution_9.run(buf34, primals_25, buf32, primals_26, buf35, 524288, grid=grid(524288), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, primals_27, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 256, 32, 32), (262144, 1, 8192, 256))
        buf37 = empty_strided_cuda((4, 128, 64, 64), (524288, 1, 8192, 128), torch.float32)
        # Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.cat]
        triton_poi_fused_cat_14.run(buf36, primals_28, buf16, buf37, 2097152, grid=grid(2097152), stream=stream0)
        del primals_28
        # Topologically Sorted Source Nodes: [out_15], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_29, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf39 = buf38; del buf38  # reuse
        # Topologically Sorted Source Nodes: [out_15], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_15.run(buf39, primals_30, 1048576, grid=grid(1048576), stream=stream0)
        del primals_30
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, buf8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf41 = buf40; del buf40  # reuse
        buf42 = reinterpret_tensor(buf36, (4, 64, 64, 64), (262144, 1, 4096, 64), 0); del buf36  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, prelu_7], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf41, primals_32, primals_33, buf42, 1048576, grid=grid(1048576), stream=stream0)
        del primals_32
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf43 = extern_kernels.convolution(buf42, buf9, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf44 = buf43; del buf43  # reuse
        buf45 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_12, out_16, out_17], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
        triton_poi_fused__prelu_kernel_add_convolution_7.run(buf44, primals_35, buf39, primals_36, buf45, 1048576, grid=grid(1048576), stream=stream0)
        del primals_35
        # Topologically Sorted Source Nodes: [out_18], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, buf10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf47 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_16.run(buf46, primals_38, buf0, buf47, 16384, 64, grid=grid(16384, 64), stream=stream0)
        del buf46
        del primals_38
    return (buf47, buf0, buf1, primals_4, buf2, primals_7, buf3, primals_10, buf4, primals_13, buf5, primals_16, buf6, primals_19, primals_20, primals_22, buf7, primals_26, primals_27, primals_29, buf8, primals_33, buf9, primals_36, buf10, buf12, buf13, buf15, buf16, buf18, buf19, buf21, buf22, buf24, buf25, buf27, buf28, buf30, buf32, buf34, buf35, buf37, buf39, buf41, buf42, buf44, buf45, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 64, 64, 64), (262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((512, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((256, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((64, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38])
    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 _Residual_Block(nn.Module):

    def __init__(self, num_chans=64):
        super(_Residual_Block, self).__init__()
        bias = True
        self.conv1 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride=
            1, padding=1, bias=bias)
        self.relu2 = nn.PReLU()
        self.conv3 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride=
            1, padding=1, bias=bias)
        self.relu4 = nn.PReLU()
        self.conv5 = nn.Conv2d(num_chans, num_chans * 2, kernel_size=3,
            stride=2, padding=1, bias=bias)
        self.relu6 = nn.PReLU()
        self.conv7 = nn.Conv2d(num_chans * 2, num_chans * 2, kernel_size=3,
            stride=1, padding=1, bias=bias)
        self.relu8 = nn.PReLU()
        self.conv9 = nn.Conv2d(num_chans * 2, num_chans * 4, kernel_size=3,
            stride=2, padding=1, bias=bias)
        self.relu10 = nn.PReLU()
        self.conv11 = nn.Conv2d(num_chans * 4, num_chans * 4, kernel_size=3,
            stride=1, padding=1, bias=bias)
        self.relu12 = nn.PReLU()
        self.conv13 = nn.Conv2d(num_chans * 4, num_chans * 8, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.up14 = nn.PixelShuffle(2)
        self.conv15 = nn.Conv2d(num_chans * 4, num_chans * 2, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.conv16 = nn.Conv2d(num_chans * 2, num_chans * 2, kernel_size=3,
            stride=1, padding=1, bias=bias)
        self.relu17 = nn.PReLU()
        self.conv18 = nn.Conv2d(num_chans * 2, num_chans * 4, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.up19 = nn.PixelShuffle(2)
        self.conv20 = nn.Conv2d(num_chans * 2, num_chans, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.conv21 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride
            =1, padding=1, bias=bias)
        self.relu22 = nn.PReLU()
        self.conv23 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride
            =1, padding=1, bias=bias)
        self.relu24 = nn.PReLU()
        self.conv25 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride
            =1, padding=1, bias=bias)

    def forward(self, x):
        res1 = x
        out = self.relu4(self.conv3(self.relu2(self.conv1(x))))
        out = torch.add(res1, out)
        cat1 = out
        out = self.relu6(self.conv5(out))
        res2 = out
        out = self.relu8(self.conv7(out))
        out = torch.add(res2, out)
        cat2 = out
        out = self.relu10(self.conv9(out))
        res3 = out
        out = self.relu12(self.conv11(out))
        out = torch.add(res3, out)
        out = self.up14(self.conv13(out))
        out = torch.cat([out, cat2], 1)
        out = self.conv15(out)
        res4 = out
        out = self.relu17(self.conv16(out))
        out = torch.add(res4, out)
        out = self.up19(self.conv18(out))
        out = torch.cat([out, cat1], 1)
        out = self.conv20(out)
        res5 = out
        out = self.relu24(self.conv23(self.relu22(self.conv21(out))))
        out = torch.add(res5, out)
        out = self.conv25(out)
        out = torch.add(out, res1)
        return out


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


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

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


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 262144 * y1), tmp0, ymask)


@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 = 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_3(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 % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_4(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 % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(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 % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_6(in_out_ptr0, 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_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_7(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, None)
    tmp6 = tl.load(in_ptr2 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = 0.0
    tmp5 = tmp2 > tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp5, tmp2, tmp8)
    tmp10 = tmp3 + tmp9
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp10, None)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_8(in_out_ptr0, 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 % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_9(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, None)
    tmp6 = tl.load(in_ptr2 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = 0.0
    tmp5 = tmp2 > tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp5, tmp2, tmp8)
    tmp10 = tmp3 + tmp9
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp10, None)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_10(in_out_ptr0, 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 % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_11(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, None)
    tmp6 = tl.load(in_ptr2 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = 0.0
    tmp5 = tmp2 > tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp5, tmp2, tmp8)
    tmp10 = tmp3 + tmp9
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp10, 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 % 256
    x1 = xindex // 256 % 32
    x2 = xindex // 8192 % 32
    x3 = xindex // 262144
    x4 = xindex // 256
    x5 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * (x2 % 2) + 4 * x0 + 512 * (x1 // 2) + 
        8192 * (x2 // 2) + 131072 * x3 + x1 % 2), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (2 * (x2 % 2) + 4 * x0 + x1 % 2), tmp4,
        eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp13 = tl.load(in_ptr2 + (128 * x4 + (-128 + x0)), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x5, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, 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 % 64
    x2 = xindex // 8192 % 64
    x3 = xindex // 524288
    x4 = xindex // 128
    x5 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * (x2 % 2) + 4 * x0 + 256 * (x1 // 2) + 
        8192 * (x2 // 2) + 262144 * x3 + x1 % 2), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (2 * (x2 % 2) + 4 * x0 + x1 % 2), tmp4,
        eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp13 = tl.load(in_ptr2 + (64 * x4 + (-64 + x0)), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x5, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused_add_convolution_16(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    xnumel = 64
    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 % 4096
    y1 = yindex // 4096
    tmp0 = tl.load(in_ptr0 + (x2 + 64 * y3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 64 * y3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (y0 + 4096 * x2 + 262144 * y1), tmp4, 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, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37, primals_38
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 64, 64), (262144, 4096, 64, 1))
    assert_size_stride(primals_2, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_6, (64,), (1,))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (1,), (1,))
    assert_size_stride(primals_11, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_12, (128,), (1,))
    assert_size_stride(primals_13, (1,), (1,))
    assert_size_stride(primals_14, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (256,), (1,))
    assert_size_stride(primals_16, (1,), (1,))
    assert_size_stride(primals_17, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_18, (256,), (1,))
    assert_size_stride(primals_19, (1,), (1,))
    assert_size_stride(primals_20, (512, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_21, (512,), (1,))
    assert_size_stride(primals_22, (128, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_25, (128,), (1,))
    assert_size_stride(primals_26, (1,), (1,))
    assert_size_stride(primals_27, (256, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_28, (256,), (1,))
    assert_size_stride(primals_29, (64, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_30, (64,), (1,))
    assert_size_stride(primals_31, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_32, (64,), (1,))
    assert_size_stride(primals_33, (1,), (1,))
    assert_size_stride(primals_34, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_35, (64,), (1,))
    assert_size_stride(primals_36, (1,), (1,))
    assert_size_stride(primals_37, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_38, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(256, 4096)](primals_1, buf0, 256, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_2, buf1, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_5, buf2, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_5
        buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_2[grid(8192, 9)](primals_8, buf3, 8192, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_3[grid(16384, 9)](primals_11, buf4, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_11
        buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_4[grid(32768, 9)](primals_14, buf5, 32768, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_14
        buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_5[grid(65536, 9)](primals_17, buf6, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_17
        buf7 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_3[grid(16384, 9)](primals_24, buf7, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_24
        buf8 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_31, buf8, 4096, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_31
        buf9 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_34, buf9, 4096, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_34
        buf10 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_1[grid(4096, 9)](primals_37, buf10, 4096, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_37
        buf11 = extern_kernels.convolution(buf0, buf1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf12 = buf11
        del buf11
        buf13 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(1048576)](buf12,
            primals_3, primals_4, buf13, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_3
        buf14 = extern_kernels.convolution(buf13, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf15 = buf14
        del buf14
        buf16 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64),
            torch.float32)
        triton_poi_fused__prelu_kernel_add_convolution_7[grid(1048576)](buf15,
            primals_6, buf0, primals_7, buf16, 1048576, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_6
        buf17 = extern_kernels.convolution(buf16, buf3, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf18 = buf17
        del buf17
        buf19 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_8[grid(524288)](buf18,
            primals_9, primals_10, buf19, 524288, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_9
        buf20 = extern_kernels.convolution(buf19, buf4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf21 = buf20
        del buf20
        buf22 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128),
            torch.float32)
        triton_poi_fused__prelu_kernel_add_convolution_9[grid(524288)](buf21,
            primals_12, buf19, primals_13, buf22, 524288, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_12
        buf23 = extern_kernels.convolution(buf22, buf5, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf23, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf24 = buf23
        del buf23
        buf25 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_10[grid(262144)](buf24,
            primals_15, primals_16, buf25, 262144, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_15
        buf26 = extern_kernels.convolution(buf25, buf6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf27 = buf26
        del buf26
        buf28 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused__prelu_kernel_add_convolution_11[grid(262144)](buf27,
            primals_18, buf25, primals_19, buf28, 262144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del primals_18
        buf29 = extern_kernels.convolution(buf28, primals_20, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 512, 16, 16), (131072, 1, 8192, 512))
        buf30 = empty_strided_cuda((4, 256, 32, 32), (262144, 1, 8192, 256),
            torch.float32)
        triton_poi_fused_cat_12[grid(1048576)](buf29, primals_21, buf22,
            buf30, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_21
        buf31 = extern_kernels.convolution(buf30, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf32 = buf31
        del buf31
        triton_poi_fused_convolution_13[grid(524288)](buf32, primals_23, 
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_23
        buf33 = extern_kernels.convolution(buf32, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf34 = buf33
        del buf33
        buf35 = reinterpret_tensor(buf29, (4, 128, 32, 32), (131072, 1, 
            4096, 128), 0)
        del buf29
        triton_poi_fused__prelu_kernel_add_convolution_9[grid(524288)](buf34,
            primals_25, buf32, primals_26, buf35, 524288, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_25
        buf36 = extern_kernels.convolution(buf35, primals_27, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 256, 32, 32), (262144, 1, 8192, 256))
        buf37 = empty_strided_cuda((4, 128, 64, 64), (524288, 1, 8192, 128),
            torch.float32)
        triton_poi_fused_cat_14[grid(2097152)](buf36, primals_28, buf16,
            buf37, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_28
        buf38 = extern_kernels.convolution(buf37, primals_29, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf39 = buf38
        del buf38
        triton_poi_fused_convolution_15[grid(1048576)](buf39, primals_30, 
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_30
        buf40 = extern_kernels.convolution(buf39, buf8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf41 = buf40
        del buf40
        buf42 = reinterpret_tensor(buf36, (4, 64, 64, 64), (262144, 1, 4096,
            64), 0)
        del buf36
        triton_poi_fused__prelu_kernel_convolution_6[grid(1048576)](buf41,
            primals_32, primals_33, buf42, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_32
        buf43 = extern_kernels.convolution(buf42, buf9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf44 = buf43
        del buf43
        buf45 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64),
            torch.float32)
        triton_poi_fused__prelu_kernel_add_convolution_7[grid(1048576)](buf44,
            primals_35, buf39, primals_36, buf45, 1048576, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_35
        buf46 = extern_kernels.convolution(buf45, buf10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf47 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_add_convolution_16[grid(16384, 64)](buf46,
            primals_38, buf0, buf47, 16384, 64, XBLOCK=64, YBLOCK=64,
            num_warps=8, num_stages=1)
        del buf46
        del primals_38
    return (buf47, buf0, buf1, primals_4, buf2, primals_7, buf3, primals_10,
        buf4, primals_13, buf5, primals_16, buf6, primals_19, primals_20,
        primals_22, buf7, primals_26, primals_27, primals_29, buf8,
        primals_33, buf9, primals_36, buf10, buf12, buf13, buf15, buf16,
        buf18, buf19, buf21, buf22, buf24, buf25, buf27, buf28, buf30,
        buf32, buf34, buf35, buf37, buf39, buf41, buf42, buf44, buf45)


class _Residual_BlockNew(nn.Module):

    def __init__(self, num_chans=64):
        super(_Residual_BlockNew, self).__init__()
        bias = True
        self.conv1 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride=
            1, padding=1, bias=bias)
        self.relu2 = nn.PReLU()
        self.conv3 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride=
            1, padding=1, bias=bias)
        self.relu4 = nn.PReLU()
        self.conv5 = nn.Conv2d(num_chans, num_chans * 2, kernel_size=3,
            stride=2, padding=1, bias=bias)
        self.relu6 = nn.PReLU()
        self.conv7 = nn.Conv2d(num_chans * 2, num_chans * 2, kernel_size=3,
            stride=1, padding=1, bias=bias)
        self.relu8 = nn.PReLU()
        self.conv9 = nn.Conv2d(num_chans * 2, num_chans * 4, kernel_size=3,
            stride=2, padding=1, bias=bias)
        self.relu10 = nn.PReLU()
        self.conv11 = nn.Conv2d(num_chans * 4, num_chans * 4, kernel_size=3,
            stride=1, padding=1, bias=bias)
        self.relu12 = nn.PReLU()
        self.conv13 = nn.Conv2d(num_chans * 4, num_chans * 8, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.up14 = nn.PixelShuffle(2)
        self.conv15 = nn.Conv2d(num_chans * 4, num_chans * 2, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.conv16 = nn.Conv2d(num_chans * 2, num_chans * 2, kernel_size=3,
            stride=1, padding=1, bias=bias)
        self.relu17 = nn.PReLU()
        self.conv18 = nn.Conv2d(num_chans * 2, num_chans * 4, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.up19 = nn.PixelShuffle(2)
        self.conv20 = nn.Conv2d(num_chans * 2, num_chans, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.conv21 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride
            =1, padding=1, bias=bias)
        self.relu22 = nn.PReLU()
        self.conv23 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride
            =1, padding=1, bias=bias)
        self.relu24 = nn.PReLU()
        self.conv25 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride
            =1, padding=1, bias=bias)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.relu2.weight
        primals_5 = self.conv3.weight
        primals_6 = self.conv3.bias
        primals_7 = self.relu4.weight
        primals_8 = self.conv5.weight
        primals_9 = self.conv5.bias
        primals_10 = self.relu6.weight
        primals_11 = self.conv7.weight
        primals_12 = self.conv7.bias
        primals_13 = self.relu8.weight
        primals_14 = self.conv9.weight
        primals_15 = self.conv9.bias
        primals_16 = self.relu10.weight
        primals_17 = self.conv11.weight
        primals_18 = self.conv11.bias
        primals_19 = self.relu12.weight
        primals_20 = self.conv13.weight
        primals_21 = self.conv13.bias
        primals_22 = self.conv15.weight
        primals_23 = self.conv15.bias
        primals_24 = self.conv16.weight
        primals_25 = self.conv16.bias
        primals_26 = self.relu17.weight
        primals_27 = self.conv18.weight
        primals_28 = self.conv18.bias
        primals_29 = self.conv20.weight
        primals_30 = self.conv20.bias
        primals_31 = self.conv21.weight
        primals_32 = self.conv21.bias
        primals_33 = self.relu22.weight
        primals_34 = self.conv23.weight
        primals_35 = self.conv23.bias
        primals_36 = self.relu24.weight
        primals_37 = self.conv25.weight
        primals_38 = self.conv25.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38])
        return output[0]
