# 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/xm/cxmk2kod6zgjturywionsuihaxqils4fvzrd7bziqpvptc3rgw43.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 4
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 4, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (16*((-1) + x1)) + (48*x2)), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/zs/czsfs6ahyese3ubiqpgbbisl635tvyjuqc3erzc4wmskprhkhyxd.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   input_1 => convolution
#   input_2 => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_3, %primals_4, [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=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_1 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[8192], 
    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_1', '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_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 96
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/mv/cmvevtdkhwfc2cmmwtwxydyesmlh6ixngyfut335uwjgjx67gl5a.py
# Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   input_5 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_9, %primals_10, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 96
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/uf/cufgpwyejxyphsulcvmt4uybqyew7btltqaajnqbr2rwjvmglk3r.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where, %where_1, %where_2], 1), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_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=[32768], 
    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_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 288
    x0 = xindex % 16
    x2 = (xindex // 4608)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 96, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (1536*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 192, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-96) + x1)) + (1536*x2)), tmp9, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 288, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + (x0 + (16*((-192) + x1)) + (1536*x2)), tmp11, other=0.0)
    tmp15 = 0.0
    tmp16 = tmp14 > tmp15
    tmp17 = tl.load(in_ptr3 + ((-192) + x1), tmp11, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 * tmp14
    tmp19 = tl.where(tmp16, tmp14, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp11, tmp19, tmp20)
    tmp22 = tl.where(tmp9, tmp10, tmp21)
    tmp23 = tl.where(tmp4, tmp5, tmp22)
    tl.store(out_ptr0 + (x3), tmp23, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/e4/ce4avpk4bjkudyp6l55wdtczkmgh7ompm234wiweom24y332sjsz.py
# Topologically Sorted Source Nodes: [input_31, input_32], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   input_31 => convolution_15
#   input_32 => gt_15, mul_15, where_15
# Graph fragment:
#   %convolution_15 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_5, %primals_48, %primals_49, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_15 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_15, 0), kwargs = {})
#   %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_15, %convolution_15), kwargs = {})
#   %where_15 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_15, %convolution_15, %mul_15), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_4 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[16384], 
    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_4', '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_4(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ct/cctjop2n3ffrqycno5j5nk2mkyqms6wanugpklbgmz5c76e3ladm.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_1 => cat_6
# Graph fragment:
#   %cat_6 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %convolution_16, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_5 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    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_cat_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 8
    x0 = xindex % 16
    x2 = (xindex // 128)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-1) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + ((-1) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tmp16 = tl.full([1], 8, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tl.load(in_ptr3 + (x0 + (16*((-5) + x1)) + (48*x2)), tmp15 & xmask, other=0.0)
    tmp19 = tl.where(tmp9, tmp14, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + (x3), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/p4/cp4a5jlq4cbmfl3sweyw54nzmfyskdqwtjh6fssswkg23w2tniol.py
# Topologically Sorted Source Nodes: [input_34, input_35], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   input_34 => convolution_17
#   input_35 => gt_16, mul_16, where_16
# Graph fragment:
#   %convolution_17 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_6, %primals_53, %primals_54, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_16 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_17, 0), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_16, %convolution_17), kwargs = {})
#   %where_16 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_16, %convolution_17, %mul_16), 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=[8192], 
    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 = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/hr/chrr2gb2jauwrwwre2wv7evrolayup7ncao4vci4fe2n6xapka32.py
# Topologically Sorted Source Nodes: [input_38], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   input_38 => convolution_19
# Graph fragment:
#   %convolution_19 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_17, %primals_59, %primals_60, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_7 = async_compile.triton('triton_poi_fused_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=[8192], 
    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_7', '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_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4s/c4sw37jqouiudkv5w7j2ozsgwfxpfhnynj6jzqmcud4d4jdpw63u.py
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_5 => cat_7
# Graph fragment:
#   %cat_7 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_16, %where_17, %where_18], 1), kwargs = {})
triton_poi_fused_cat_8 = async_compile.triton('triton_poi_fused_cat_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_cat_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 24576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 384
    x0 = xindex % 16
    x2 = (xindex // 6144)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (2048*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 256, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-128) + x1)) + (2048*x2)), tmp9, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 384, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + (x0 + (16*((-256) + x1)) + (2048*x2)), tmp11, other=0.0)
    tmp15 = 0.0
    tmp16 = tmp14 > tmp15
    tmp17 = tl.load(in_ptr3 + ((-256) + x1), tmp11, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 * tmp14
    tmp19 = tl.where(tmp16, tmp14, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp11, tmp19, tmp20)
    tmp22 = tl.where(tmp9, tmp10, tmp21)
    tmp23 = tl.where(tmp4, tmp5, tmp22)
    tl.store(out_ptr0 + (x3), tmp23, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/se/cseugy6yibv2paeqa72icgc2454rfxzhy3lgd3s473bai6dv6nu2.py
# Topologically Sorted Source Nodes: [input_64, input_65], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   input_64 => convolution_32
#   input_65 => gt_31, mul_31, where_31
# Graph fragment:
#   %convolution_32 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_11, %primals_98, %primals_99, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_31 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_32, 0), kwargs = {})
#   %mul_31 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_31, %convolution_32), kwargs = {})
#   %where_31 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_31, %convolution_32, %mul_31), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_9 = async_compile.triton('triton_poi_fused__prelu_kernel_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=[32768], 
    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_9', '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_9(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 512
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/gv/cgvoi43bpqyynthafx6tebrihvw377v4h3qwua5ufjwyy5jej2lw.py
# Topologically Sorted Source Nodes: [input_66], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   input_66 => convolution_33
# Graph fragment:
#   %convolution_33 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_31, %primals_101, %primals_102, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_10 = async_compile.triton('triton_poi_fused_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=[256], 
    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_10', '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_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, 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, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73, primals_74, primals_75, primals_76, primals_77, primals_78, primals_79, primals_80, primals_81, primals_82, primals_83, primals_84, primals_85, primals_86, primals_87, primals_88, primals_89, primals_90, primals_91, primals_92, primals_93, primals_94, primals_95, primals_96, primals_97, primals_98, primals_99, primals_100, primals_101, primals_102 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_3, (96, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (96, ), (1, ))
    assert_size_stride(primals_5, (96, ), (1, ))
    assert_size_stride(primals_6, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_7, (96, ), (1, ))
    assert_size_stride(primals_8, (96, ), (1, ))
    assert_size_stride(primals_9, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_10, (96, ), (1, ))
    assert_size_stride(primals_11, (96, ), (1, ))
    assert_size_stride(primals_12, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_13, (96, ), (1, ))
    assert_size_stride(primals_14, (96, ), (1, ))
    assert_size_stride(primals_15, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_16, (96, ), (1, ))
    assert_size_stride(primals_17, (96, ), (1, ))
    assert_size_stride(primals_18, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_19, (96, ), (1, ))
    assert_size_stride(primals_20, (96, ), (1, ))
    assert_size_stride(primals_21, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_22, (96, ), (1, ))
    assert_size_stride(primals_23, (96, ), (1, ))
    assert_size_stride(primals_24, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_25, (96, ), (1, ))
    assert_size_stride(primals_26, (96, ), (1, ))
    assert_size_stride(primals_27, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_28, (96, ), (1, ))
    assert_size_stride(primals_29, (96, ), (1, ))
    assert_size_stride(primals_30, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_31, (96, ), (1, ))
    assert_size_stride(primals_32, (96, ), (1, ))
    assert_size_stride(primals_33, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_34, (96, ), (1, ))
    assert_size_stride(primals_35, (96, ), (1, ))
    assert_size_stride(primals_36, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_37, (96, ), (1, ))
    assert_size_stride(primals_38, (96, ), (1, ))
    assert_size_stride(primals_39, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_40, (96, ), (1, ))
    assert_size_stride(primals_41, (96, ), (1, ))
    assert_size_stride(primals_42, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_43, (96, ), (1, ))
    assert_size_stride(primals_44, (96, ), (1, ))
    assert_size_stride(primals_45, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_46, (96, ), (1, ))
    assert_size_stride(primals_47, (96, ), (1, ))
    assert_size_stride(primals_48, (256, 288, 1, 1), (288, 1, 1, 1))
    assert_size_stride(primals_49, (256, ), (1, ))
    assert_size_stride(primals_50, (256, ), (1, ))
    assert_size_stride(primals_51, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_52, (4, ), (1, ))
    assert_size_stride(primals_53, (128, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_54, (128, ), (1, ))
    assert_size_stride(primals_55, (128, ), (1, ))
    assert_size_stride(primals_56, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_57, (128, ), (1, ))
    assert_size_stride(primals_58, (128, ), (1, ))
    assert_size_stride(primals_59, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_60, (128, ), (1, ))
    assert_size_stride(primals_61, (128, ), (1, ))
    assert_size_stride(primals_62, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_63, (128, ), (1, ))
    assert_size_stride(primals_64, (128, ), (1, ))
    assert_size_stride(primals_65, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_66, (128, ), (1, ))
    assert_size_stride(primals_67, (128, ), (1, ))
    assert_size_stride(primals_68, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_69, (128, ), (1, ))
    assert_size_stride(primals_70, (128, ), (1, ))
    assert_size_stride(primals_71, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_72, (128, ), (1, ))
    assert_size_stride(primals_73, (128, ), (1, ))
    assert_size_stride(primals_74, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_75, (128, ), (1, ))
    assert_size_stride(primals_76, (128, ), (1, ))
    assert_size_stride(primals_77, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_78, (128, ), (1, ))
    assert_size_stride(primals_79, (128, ), (1, ))
    assert_size_stride(primals_80, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_81, (128, ), (1, ))
    assert_size_stride(primals_82, (128, ), (1, ))
    assert_size_stride(primals_83, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_84, (128, ), (1, ))
    assert_size_stride(primals_85, (128, ), (1, ))
    assert_size_stride(primals_86, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_87, (128, ), (1, ))
    assert_size_stride(primals_88, (128, ), (1, ))
    assert_size_stride(primals_89, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_90, (128, ), (1, ))
    assert_size_stride(primals_91, (128, ), (1, ))
    assert_size_stride(primals_92, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_93, (128, ), (1, ))
    assert_size_stride(primals_94, (128, ), (1, ))
    assert_size_stride(primals_95, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_96, (128, ), (1, ))
    assert_size_stride(primals_97, (128, ), (1, ))
    assert_size_stride(primals_98, (512, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_99, (512, ), (1, ))
    assert_size_stride(primals_100, (512, ), (1, ))
    assert_size_stride(primals_101, (4, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_102, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf2 = buf1; del buf1  # reuse
        buf3 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf2, primals_4, primals_5, buf3, 6144, grid=grid(6144), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf5 = buf4; del buf4  # reuse
        buf6 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_3, input_4], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf5, primals_7, primals_8, buf6, 6144, grid=grid(6144), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_9, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf8, primals_10, 6144, grid=grid(6144), stream=stream0)
        del primals_10
        buf9 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf3, buf6, buf8, primals_11, buf9, 18432, grid=grid(18432), stream=stream0)
        # Topologically Sorted Source Nodes: [input_7], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf11 = buf10; del buf10  # reuse
        buf12 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_7, input_8], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf11, primals_13, primals_14, buf12, 6144, grid=grid(6144), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [input_9], Original ATen: [aten.convolution]
        buf13 = extern_kernels.convolution(buf12, primals_15, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf13, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf14 = buf13; del buf13  # reuse
        buf15 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_9, input_10], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf14, primals_16, primals_17, buf15, 6144, grid=grid(6144), stream=stream0)
        del primals_16
        # Topologically Sorted Source Nodes: [input_11], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [input_11], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf17, primals_19, 6144, grid=grid(6144), stream=stream0)
        del primals_19
        buf18 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf12, buf15, buf17, primals_20, buf18, 18432, grid=grid(18432), stream=stream0)
        # Topologically Sorted Source Nodes: [input_13], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_21, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf20 = buf19; del buf19  # reuse
        buf21 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_13, input_14], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf20, primals_22, primals_23, buf21, 6144, grid=grid(6144), stream=stream0)
        del primals_22
        # Topologically Sorted Source Nodes: [input_15], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf23 = buf22; del buf22  # reuse
        buf24 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_15, input_16], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf23, primals_25, primals_26, buf24, 6144, grid=grid(6144), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [input_17], Original ATen: [aten.convolution]
        buf25 = extern_kernels.convolution(buf24, primals_27, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf25, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf26 = buf25; del buf25  # reuse
        # Topologically Sorted Source Nodes: [input_17], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf26, primals_28, 6144, grid=grid(6144), stream=stream0)
        del primals_28
        buf27 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf21, buf24, buf26, primals_29, buf27, 18432, grid=grid(18432), stream=stream0)
        # Topologically Sorted Source Nodes: [input_19], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf29 = buf28; del buf28  # reuse
        buf30 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_19, input_20], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf29, primals_31, primals_32, buf30, 6144, grid=grid(6144), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [input_21], Original ATen: [aten.convolution]
        buf31 = extern_kernels.convolution(buf30, primals_33, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf32 = buf31; del buf31  # reuse
        buf33 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_21, input_22], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf32, primals_34, primals_35, buf33, 6144, grid=grid(6144), stream=stream0)
        del primals_34
        # Topologically Sorted Source Nodes: [input_23], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf33, primals_36, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [input_23], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf35, primals_37, 6144, grid=grid(6144), stream=stream0)
        del primals_37
        buf36 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf30, buf33, buf35, primals_38, buf36, 18432, grid=grid(18432), stream=stream0)
        # Topologically Sorted Source Nodes: [input_25], Original ATen: [aten.convolution]
        buf37 = extern_kernels.convolution(buf36, primals_39, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf37, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf38 = buf37; del buf37  # reuse
        buf39 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_25, input_26], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf38, primals_40, primals_41, buf39, 6144, grid=grid(6144), stream=stream0)
        del primals_40
        # Topologically Sorted Source Nodes: [input_27], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, primals_42, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf41 = buf40; del buf40  # reuse
        buf42 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_27, input_28], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_1.run(buf41, primals_43, primals_44, buf42, 6144, grid=grid(6144), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [input_29], Original ATen: [aten.convolution]
        buf43 = extern_kernels.convolution(buf42, primals_45, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf44 = buf43; del buf43  # reuse
        # Topologically Sorted Source Nodes: [input_29], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf44, primals_46, 6144, grid=grid(6144), stream=stream0)
        del primals_46
        buf45 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf39, buf42, buf44, primals_47, buf45, 18432, grid=grid(18432), stream=stream0)
        # Topologically Sorted Source Nodes: [input_31], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_48, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf47 = buf46; del buf46  # reuse
        buf48 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_31, input_32], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_4.run(buf47, primals_49, primals_50, buf48, 16384, grid=grid(16384), stream=stream0)
        del primals_49
        # Topologically Sorted Source Nodes: [input_33], Original ATen: [aten.convolution]
        buf49 = extern_kernels.convolution(buf48, primals_51, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf49, (4, 4, 4, 4), (64, 16, 4, 1))
        buf50 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_5.run(primals_1, buf49, primals_52, primals_2, buf50, 512, grid=grid(512), stream=stream0)
        del buf49
        del primals_1
        del primals_2
        del primals_52
        # Topologically Sorted Source Nodes: [input_34], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf50, primals_53, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf52 = buf51; del buf51  # reuse
        buf53 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_34, input_35], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf52, primals_54, primals_55, buf53, 8192, grid=grid(8192), stream=stream0)
        del primals_54
        # Topologically Sorted Source Nodes: [input_36], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf53, primals_56, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf55 = buf54; del buf54  # reuse
        buf56 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_36, input_37], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf55, primals_57, primals_58, buf56, 8192, grid=grid(8192), stream=stream0)
        del primals_57
        # Topologically Sorted Source Nodes: [input_38], Original ATen: [aten.convolution]
        buf57 = extern_kernels.convolution(buf56, primals_59, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf57, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf58 = buf57; del buf57  # reuse
        # Topologically Sorted Source Nodes: [input_38], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf58, primals_60, 8192, grid=grid(8192), stream=stream0)
        del primals_60
        buf59 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.cat]
        triton_poi_fused_cat_8.run(buf53, buf56, buf58, primals_61, buf59, 24576, grid=grid(24576), stream=stream0)
        # Topologically Sorted Source Nodes: [input_40], Original ATen: [aten.convolution]
        buf60 = extern_kernels.convolution(buf59, primals_62, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf60, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf61 = buf60; del buf60  # reuse
        buf62 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_40, input_41], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf61, primals_63, primals_64, buf62, 8192, grid=grid(8192), stream=stream0)
        del primals_63
        # Topologically Sorted Source Nodes: [input_42], Original ATen: [aten.convolution]
        buf63 = extern_kernels.convolution(buf62, primals_65, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf64 = buf63; del buf63  # reuse
        buf65 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_42, input_43], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf64, primals_66, primals_67, buf65, 8192, grid=grid(8192), stream=stream0)
        del primals_66
        # Topologically Sorted Source Nodes: [input_44], Original ATen: [aten.convolution]
        buf66 = extern_kernels.convolution(buf65, primals_68, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf67 = buf66; del buf66  # reuse
        # Topologically Sorted Source Nodes: [input_44], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf67, primals_69, 8192, grid=grid(8192), stream=stream0)
        del primals_69
        buf68 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.cat]
        triton_poi_fused_cat_8.run(buf62, buf65, buf67, primals_70, buf68, 24576, grid=grid(24576), stream=stream0)
        # Topologically Sorted Source Nodes: [input_46], Original ATen: [aten.convolution]
        buf69 = extern_kernels.convolution(buf68, primals_71, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf70 = buf69; del buf69  # reuse
        buf71 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_46, input_47], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf70, primals_72, primals_73, buf71, 8192, grid=grid(8192), stream=stream0)
        del primals_72
        # Topologically Sorted Source Nodes: [input_48], Original ATen: [aten.convolution]
        buf72 = extern_kernels.convolution(buf71, primals_74, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf72, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf73 = buf72; del buf72  # reuse
        buf74 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_48, input_49], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf73, primals_75, primals_76, buf74, 8192, grid=grid(8192), stream=stream0)
        del primals_75
        # Topologically Sorted Source Nodes: [input_50], Original ATen: [aten.convolution]
        buf75 = extern_kernels.convolution(buf74, primals_77, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf75, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf76 = buf75; del buf75  # reuse
        # Topologically Sorted Source Nodes: [input_50], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf76, primals_78, 8192, grid=grid(8192), stream=stream0)
        del primals_78
        buf77 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_7], Original ATen: [aten.cat]
        triton_poi_fused_cat_8.run(buf71, buf74, buf76, primals_79, buf77, 24576, grid=grid(24576), stream=stream0)
        # Topologically Sorted Source Nodes: [input_52], Original ATen: [aten.convolution]
        buf78 = extern_kernels.convolution(buf77, primals_80, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf79 = buf78; del buf78  # reuse
        buf80 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_52, input_53], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf79, primals_81, primals_82, buf80, 8192, grid=grid(8192), stream=stream0)
        del primals_81
        # Topologically Sorted Source Nodes: [input_54], Original ATen: [aten.convolution]
        buf81 = extern_kernels.convolution(buf80, primals_83, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf81, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf82 = buf81; del buf81  # reuse
        buf83 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_54, input_55], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf82, primals_84, primals_85, buf83, 8192, grid=grid(8192), stream=stream0)
        del primals_84
        # Topologically Sorted Source Nodes: [input_56], Original ATen: [aten.convolution]
        buf84 = extern_kernels.convolution(buf83, primals_86, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf84, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf85 = buf84; del buf84  # reuse
        # Topologically Sorted Source Nodes: [input_56], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf85, primals_87, 8192, grid=grid(8192), stream=stream0)
        del primals_87
        buf86 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.cat]
        triton_poi_fused_cat_8.run(buf80, buf83, buf85, primals_88, buf86, 24576, grid=grid(24576), stream=stream0)
        # Topologically Sorted Source Nodes: [input_58], Original ATen: [aten.convolution]
        buf87 = extern_kernels.convolution(buf86, primals_89, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf88 = buf87; del buf87  # reuse
        buf89 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_58, input_59], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf88, primals_90, primals_91, buf89, 8192, grid=grid(8192), stream=stream0)
        del primals_90
        # Topologically Sorted Source Nodes: [input_60], Original ATen: [aten.convolution]
        buf90 = extern_kernels.convolution(buf89, primals_92, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf90, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf91 = buf90; del buf90  # reuse
        buf92 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_60, input_61], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_6.run(buf91, primals_93, primals_94, buf92, 8192, grid=grid(8192), stream=stream0)
        del primals_93
        # Topologically Sorted Source Nodes: [input_62], Original ATen: [aten.convolution]
        buf93 = extern_kernels.convolution(buf92, primals_95, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf93, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf94 = buf93; del buf93  # reuse
        # Topologically Sorted Source Nodes: [input_62], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf94, primals_96, 8192, grid=grid(8192), stream=stream0)
        del primals_96
        buf95 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.cat]
        triton_poi_fused_cat_8.run(buf89, buf92, buf94, primals_97, buf95, 24576, grid=grid(24576), stream=stream0)
        # Topologically Sorted Source Nodes: [input_64], Original ATen: [aten.convolution]
        buf96 = extern_kernels.convolution(buf95, primals_98, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf96, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf97 = buf96; del buf96  # reuse
        buf98 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_64, input_65], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_9.run(buf97, primals_99, primals_100, buf98, 32768, grid=grid(32768), stream=stream0)
        del primals_99
        # Topologically Sorted Source Nodes: [input_66], Original ATen: [aten.convolution]
        buf99 = extern_kernels.convolution(buf98, primals_101, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf99, (4, 4, 4, 4), (64, 16, 4, 1))
        buf100 = buf99; del buf99  # reuse
        # Topologically Sorted Source Nodes: [input_66], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_10.run(buf100, primals_102, 256, grid=grid(256), stream=stream0)
        del primals_102
    return (buf100, primals_3, primals_5, primals_6, primals_8, primals_9, primals_11, primals_12, primals_14, primals_15, primals_17, primals_18, primals_20, primals_21, primals_23, primals_24, primals_26, primals_27, primals_29, primals_30, primals_32, primals_33, primals_35, primals_36, primals_38, primals_39, primals_41, primals_42, primals_44, primals_45, primals_47, primals_48, primals_50, primals_51, primals_53, primals_55, primals_56, primals_58, primals_59, primals_61, primals_62, primals_64, primals_65, primals_67, primals_68, primals_70, primals_71, primals_73, primals_74, primals_76, primals_77, primals_79, primals_80, primals_82, primals_83, primals_85, primals_86, primals_88, primals_89, primals_91, primals_92, primals_94, primals_95, primals_97, primals_98, primals_100, primals_101, buf0, buf2, buf3, buf5, buf6, buf8, buf9, buf11, buf12, buf14, buf15, buf17, buf18, buf20, buf21, buf23, buf24, buf26, buf27, buf29, buf30, buf32, buf33, buf35, buf36, buf38, buf39, buf41, buf42, buf44, buf45, buf47, buf48, buf50, buf52, buf53, buf55, buf56, buf58, buf59, buf61, buf62, buf64, buf65, buf67, buf68, buf70, buf71, buf73, buf74, buf76, buf77, buf79, buf80, buf82, buf83, buf85, buf86, buf88, buf89, buf91, buf92, buf94, buf95, buf97, buf98, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((96, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((96, 288, 3, 3), (2592, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((96, 288, 3, 3), (2592, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((96, 288, 3, 3), (2592, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((96, 288, 3, 3), (2592, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((256, 288, 1, 1), (288, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((4, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((128, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_55 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_56 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_57 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_58 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_59 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_60 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_61 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_62 = rand_strided((128, 384, 3, 3), (3456, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_63 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_64 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_65 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_66 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_67 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_68 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_69 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_70 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_71 = rand_strided((128, 384, 3, 3), (3456, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_72 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_73 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_74 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_75 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_76 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_77 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_78 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_79 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_80 = rand_strided((128, 384, 3, 3), (3456, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_81 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_82 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_83 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_84 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_85 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_86 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_87 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_88 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_89 = rand_strided((128, 384, 3, 3), (3456, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_90 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_91 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_92 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_93 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_94 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_95 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_96 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_97 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_98 = rand_strided((512, 384, 1, 1), (384, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_99 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_100 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_101 = rand_strided((4, 512, 1, 1), (512, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_102 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73, primals_74, primals_75, primals_76, primals_77, primals_78, primals_79, primals_80, primals_81, primals_82, primals_83, primals_84, primals_85, primals_86, primals_87, primals_88, primals_89, primals_90, primals_91, primals_92, primals_93, primals_94, primals_95, primals_96, primals_97, primals_98, primals_99, primals_100, primals_101, primals_102])
    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
from itertools import chain as chain
import torch.utils.data
from collections import OrderedDict
import torch.hub
import torch.nn.parallel
import torch.optim


class concatLayer(nn.Module):

    def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
        super(concatLayer, self).__init__()
        self.firstSub = self.concatLayerSub(in_channels,
            out_channels_perSub, '%d_stage%d_' % (i, j) + appendix + '_0')
        self.secondSub = self.concatLayerSub(out_channels_perSub,
            out_channels_perSub, '%d_stage%d_' % (i, j) + appendix + '_1')
        self.thirdSub = self.concatLayerSub(out_channels_perSub,
            out_channels_perSub, '%d_stage%d_' % (i, j) + appendix + '_2')

    def forward(self, x):
        firstSub = self.firstSub(x)
        secondSub = self.secondSub(firstSub)
        thirdSub = self.thirdSub(secondSub)
        out = torch.cat([firstSub, secondSub, thirdSub], 1)
        return out

    def concatLayerSub(self, in_channels, out_channels, layerName):
        concatLayerSubOrdered = OrderedDict()
        conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        concatLayerSubOrdered.update({('Mconv' + layerName): conv2d})
        concatLayerSubOrdered.update({('Mprelu' + layerName): nn.PReLU(
            out_channels)})
        return nn.Sequential(concatLayerSubOrdered)


class stage(nn.Module):

    def __init__(self, stageID, in_channels, out_channels_perSub,
        mid_channels, out_channels, appendix):
        super(stage, self).__init__()
        self.firstConcat = concatLayer(in_channels, out_channels_perSub, 1,
            stageID, appendix)
        self.secondConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 2, stageID, appendix)
        self.thirdConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 3, stageID, appendix)
        self.fourthConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 4, stageID, appendix)
        self.fifthConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 5, stageID, appendix)
        conv2d = nn.Conv2d(3 * out_channels_perSub, mid_channels,
            kernel_size=1, padding=0)
        prelu = nn.PReLU(mid_channels)
        self.afterConcatsFirst = nn.Sequential(OrderedDict({(
            'Mconv6_stage%d_%s' % (stageID, appendix)): conv2d, (
            'Mprelu6_stage%d_%s' % (stageID, appendix)): prelu}))
        conv2d = nn.Conv2d(mid_channels, out_channels, kernel_size=1, padding=0
            )
        self.afterConcatsSecond = nn.Sequential(OrderedDict({(
            'Mconv7_stage%d_%s' % (stageID, appendix)): conv2d}))

    def forward(self, x):
        x = self.firstConcat(x)
        x = self.secondConcat(x)
        x = self.thirdConcat(x)
        x = self.fourthConcat(x)
        x = self.fifthConcat(x)
        x = self.afterConcatsFirst(x)
        out = self.afterConcatsSecond(x)
        return out


class L1Part(nn.Module):

    def __init__(self, in_channels, stage_out_channels):
        super(L1Part, self).__init__()
        self.firstStage = stage(0, in_channels, 96, 256, stage_out_channels,
            'L1')
        self.secondStage = stage(1, in_channels + stage_out_channels, 128, 
            512, stage_out_channels, 'L1')

    def forward(self, features, L2Out):
        x = torch.cat([features, L2Out], 1)
        x = self.firstStage(x)
        x = torch.cat([features, x, L2Out], 1)
        out = self.secondStage(x)
        return out


def get_inputs():
    return [torch.rand([4, 1, 4, 4]), torch.rand([4, 3, 4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'stage_out_channels': 4}]

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
from itertools import chain as chain
import torch.utils.data
from collections import OrderedDict
import torch.hub
import torch.nn.parallel
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-1 + x1) + 48 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_1(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)
    x3 = xindex
    x1 = xindex // 16 % 96
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 96
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 288
    x0 = xindex % 16
    x2 = xindex // 4608
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 96, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 1536 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 192, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-96 + x1) + 1536 * x2), tmp9,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 288, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 16 * (-192 + x1) + 1536 * x2), tmp11,
        other=0.0)
    tmp15 = 0.0
    tmp16 = tmp14 > tmp15
    tmp17 = tl.load(in_ptr3 + (-192 + x1), tmp11, eviction_policy=
        'evict_last', other=0.0)
    tmp18 = tmp17 * tmp14
    tmp19 = tl.where(tmp16, tmp14, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp11, tmp19, tmp20)
    tmp22 = tl.where(tmp9, tmp10, tmp21)
    tmp23 = tl.where(tmp4, tmp5, tmp22)
    tl.store(out_ptr0 + x3, tmp23, None)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_4(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)
    x3 = xindex
    x1 = xindex // 16 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-1 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-1 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tl.full([1], 8, tl.int64)
    tmp18 = tl.load(in_ptr3 + (x0 + 16 * (-5 + x1) + 48 * x2), tmp15 &
        xmask, other=0.0)
    tmp19 = tl.where(tmp9, tmp14, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + x3, tmp20, 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)
    x3 = xindex
    x1 = xindex // 16 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_cat_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 384
    x0 = xindex % 16
    x2 = xindex // 6144
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 2048 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 256, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-128 + x1) + 2048 * x2), tmp9,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 384, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 16 * (-256 + x1) + 2048 * x2), tmp11,
        other=0.0)
    tmp15 = 0.0
    tmp16 = tmp14 > tmp15
    tmp17 = tl.load(in_ptr3 + (-256 + x1), tmp11, eviction_policy=
        'evict_last', other=0.0)
    tmp18 = tmp17 * tmp14
    tmp19 = tl.where(tmp16, tmp14, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp11, tmp19, tmp20)
    tmp22 = tl.where(tmp9, tmp10, tmp21)
    tmp23 = tl.where(tmp4, tmp5, tmp22)
    tl.store(out_ptr0 + x3, tmp23, None)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_9(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)
    x3 = xindex
    x1 = xindex // 16 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, 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, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52,
        primals_53, primals_54, primals_55, primals_56, primals_57,
        primals_58, primals_59, primals_60, primals_61, primals_62,
        primals_63, primals_64, primals_65, primals_66, primals_67,
        primals_68, primals_69, primals_70, primals_71, primals_72,
        primals_73, primals_74, primals_75, primals_76, primals_77,
        primals_78, primals_79, primals_80, primals_81, primals_82,
        primals_83, primals_84, primals_85, primals_86, primals_87,
        primals_88, primals_89, primals_90, primals_91, primals_92,
        primals_93, primals_94, primals_95, primals_96, primals_97,
        primals_98, primals_99, primals_100, primals_101, primals_102) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_3, (96, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (96,), (1,))
    assert_size_stride(primals_5, (96,), (1,))
    assert_size_stride(primals_6, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_7, (96,), (1,))
    assert_size_stride(primals_8, (96,), (1,))
    assert_size_stride(primals_9, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_10, (96,), (1,))
    assert_size_stride(primals_11, (96,), (1,))
    assert_size_stride(primals_12, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_13, (96,), (1,))
    assert_size_stride(primals_14, (96,), (1,))
    assert_size_stride(primals_15, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_16, (96,), (1,))
    assert_size_stride(primals_17, (96,), (1,))
    assert_size_stride(primals_18, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_19, (96,), (1,))
    assert_size_stride(primals_20, (96,), (1,))
    assert_size_stride(primals_21, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_22, (96,), (1,))
    assert_size_stride(primals_23, (96,), (1,))
    assert_size_stride(primals_24, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_25, (96,), (1,))
    assert_size_stride(primals_26, (96,), (1,))
    assert_size_stride(primals_27, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_28, (96,), (1,))
    assert_size_stride(primals_29, (96,), (1,))
    assert_size_stride(primals_30, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_31, (96,), (1,))
    assert_size_stride(primals_32, (96,), (1,))
    assert_size_stride(primals_33, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_34, (96,), (1,))
    assert_size_stride(primals_35, (96,), (1,))
    assert_size_stride(primals_36, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_37, (96,), (1,))
    assert_size_stride(primals_38, (96,), (1,))
    assert_size_stride(primals_39, (96, 288, 3, 3), (2592, 9, 3, 1))
    assert_size_stride(primals_40, (96,), (1,))
    assert_size_stride(primals_41, (96,), (1,))
    assert_size_stride(primals_42, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_43, (96,), (1,))
    assert_size_stride(primals_44, (96,), (1,))
    assert_size_stride(primals_45, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_46, (96,), (1,))
    assert_size_stride(primals_47, (96,), (1,))
    assert_size_stride(primals_48, (256, 288, 1, 1), (288, 1, 1, 1))
    assert_size_stride(primals_49, (256,), (1,))
    assert_size_stride(primals_50, (256,), (1,))
    assert_size_stride(primals_51, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_52, (4,), (1,))
    assert_size_stride(primals_53, (128, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_54, (128,), (1,))
    assert_size_stride(primals_55, (128,), (1,))
    assert_size_stride(primals_56, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_57, (128,), (1,))
    assert_size_stride(primals_58, (128,), (1,))
    assert_size_stride(primals_59, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_60, (128,), (1,))
    assert_size_stride(primals_61, (128,), (1,))
    assert_size_stride(primals_62, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_63, (128,), (1,))
    assert_size_stride(primals_64, (128,), (1,))
    assert_size_stride(primals_65, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_66, (128,), (1,))
    assert_size_stride(primals_67, (128,), (1,))
    assert_size_stride(primals_68, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_69, (128,), (1,))
    assert_size_stride(primals_70, (128,), (1,))
    assert_size_stride(primals_71, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_72, (128,), (1,))
    assert_size_stride(primals_73, (128,), (1,))
    assert_size_stride(primals_74, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_75, (128,), (1,))
    assert_size_stride(primals_76, (128,), (1,))
    assert_size_stride(primals_77, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_78, (128,), (1,))
    assert_size_stride(primals_79, (128,), (1,))
    assert_size_stride(primals_80, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_81, (128,), (1,))
    assert_size_stride(primals_82, (128,), (1,))
    assert_size_stride(primals_83, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_84, (128,), (1,))
    assert_size_stride(primals_85, (128,), (1,))
    assert_size_stride(primals_86, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_87, (128,), (1,))
    assert_size_stride(primals_88, (128,), (1,))
    assert_size_stride(primals_89, (128, 384, 3, 3), (3456, 9, 3, 1))
    assert_size_stride(primals_90, (128,), (1,))
    assert_size_stride(primals_91, (128,), (1,))
    assert_size_stride(primals_92, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_93, (128,), (1,))
    assert_size_stride(primals_94, (128,), (1,))
    assert_size_stride(primals_95, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_96, (128,), (1,))
    assert_size_stride(primals_97, (128,), (1,))
    assert_size_stride(primals_98, (512, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_99, (512,), (1,))
    assert_size_stride(primals_100, (512,), (1,))
    assert_size_stride(primals_101, (4, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_102, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(256)](primals_1, primals_2, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf2,
            primals_4, primals_5, buf3, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_4
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf5,
            primals_7, primals_8, buf6, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_7
        buf7 = extern_kernels.convolution(buf6, primals_9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_2[grid(6144)](buf8, primals_10, 6144,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_10
        buf9 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(18432)](buf3, buf6, buf8, primals_11,
            buf9, 18432, XBLOCK=128, num_warps=4, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf11,
            primals_13, primals_14, buf12, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_13
        buf13 = extern_kernels.convolution(buf12, primals_15, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf13, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf14 = buf13
        del buf13
        buf15 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf14,
            primals_16, primals_17, buf15, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_16
        buf16 = extern_kernels.convolution(buf15, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_2[grid(6144)](buf17, primals_19, 6144,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_19
        buf18 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(18432)](buf12, buf15, buf17, primals_20,
            buf18, 18432, XBLOCK=128, num_warps=4, num_stages=1)
        buf19 = extern_kernels.convolution(buf18, primals_21, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf20 = buf19
        del buf19
        buf21 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf20,
            primals_22, primals_23, buf21, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_22
        buf22 = extern_kernels.convolution(buf21, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf23 = buf22
        del buf22
        buf24 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf23,
            primals_25, primals_26, buf24, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_25
        buf25 = extern_kernels.convolution(buf24, primals_27, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf25, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf26 = buf25
        del buf25
        triton_poi_fused_convolution_2[grid(6144)](buf26, primals_28, 6144,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_28
        buf27 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(18432)](buf21, buf24, buf26, primals_29,
            buf27, 18432, XBLOCK=128, num_warps=4, num_stages=1)
        buf28 = extern_kernels.convolution(buf27, primals_30, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf29 = buf28
        del buf28
        buf30 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf29,
            primals_31, primals_32, buf30, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_31
        buf31 = extern_kernels.convolution(buf30, primals_33, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf32 = buf31
        del buf31
        buf33 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf32,
            primals_34, primals_35, buf33, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_34
        buf34 = extern_kernels.convolution(buf33, primals_36, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf35 = buf34
        del buf34
        triton_poi_fused_convolution_2[grid(6144)](buf35, primals_37, 6144,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_37
        buf36 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(18432)](buf30, buf33, buf35, primals_38,
            buf36, 18432, XBLOCK=128, num_warps=4, num_stages=1)
        buf37 = extern_kernels.convolution(buf36, primals_39, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf37, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf38 = buf37
        del buf37
        buf39 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf38,
            primals_40, primals_41, buf39, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_40
        buf40 = extern_kernels.convolution(buf39, primals_42, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf41 = buf40
        del buf40
        buf42 = empty_strided_cuda((4, 96, 4, 4), (1536, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(6144)](buf41,
            primals_43, primals_44, buf42, 6144, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_43
        buf43 = extern_kernels.convolution(buf42, primals_45, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 96, 4, 4), (1536, 16, 4, 1))
        buf44 = buf43
        del buf43
        triton_poi_fused_convolution_2[grid(6144)](buf44, primals_46, 6144,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_46
        buf45 = empty_strided_cuda((4, 288, 4, 4), (4608, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(18432)](buf39, buf42, buf44, primals_47,
            buf45, 18432, XBLOCK=128, num_warps=4, num_stages=1)
        buf46 = extern_kernels.convolution(buf45, primals_48, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf47 = buf46
        del buf46
        buf48 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_4[grid(16384)](buf47,
            primals_49, primals_50, buf48, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_49
        buf49 = extern_kernels.convolution(buf48, primals_51, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf49, (4, 4, 4, 4), (64, 16, 4, 1))
        buf50 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_5[grid(512)](primals_1, buf49, primals_52,
            primals_2, buf50, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del buf49
        del primals_1
        del primals_2
        del primals_52
        buf51 = extern_kernels.convolution(buf50, primals_53, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf52 = buf51
        del buf51
        buf53 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf52,
            primals_54, primals_55, buf53, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_54
        buf54 = extern_kernels.convolution(buf53, primals_56, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf55 = buf54
        del buf54
        buf56 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf55,
            primals_57, primals_58, buf56, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_57
        buf57 = extern_kernels.convolution(buf56, primals_59, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf57, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf58 = buf57
        del buf57
        triton_poi_fused_convolution_7[grid(8192)](buf58, primals_60, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_60
        buf59 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_8[grid(24576)](buf53, buf56, buf58, primals_61,
            buf59, 24576, XBLOCK=256, num_warps=4, num_stages=1)
        buf60 = extern_kernels.convolution(buf59, primals_62, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf60, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf61 = buf60
        del buf60
        buf62 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf61,
            primals_63, primals_64, buf62, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_63
        buf63 = extern_kernels.convolution(buf62, primals_65, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf64 = buf63
        del buf63
        buf65 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf64,
            primals_66, primals_67, buf65, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_66
        buf66 = extern_kernels.convolution(buf65, primals_68, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf67 = buf66
        del buf66
        triton_poi_fused_convolution_7[grid(8192)](buf67, primals_69, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_69
        buf68 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_8[grid(24576)](buf62, buf65, buf67, primals_70,
            buf68, 24576, XBLOCK=256, num_warps=4, num_stages=1)
        buf69 = extern_kernels.convolution(buf68, primals_71, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf70 = buf69
        del buf69
        buf71 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf70,
            primals_72, primals_73, buf71, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_72
        buf72 = extern_kernels.convolution(buf71, primals_74, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf72, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf73 = buf72
        del buf72
        buf74 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf73,
            primals_75, primals_76, buf74, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_75
        buf75 = extern_kernels.convolution(buf74, primals_77, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf75, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf76 = buf75
        del buf75
        triton_poi_fused_convolution_7[grid(8192)](buf76, primals_78, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_78
        buf77 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_8[grid(24576)](buf71, buf74, buf76, primals_79,
            buf77, 24576, XBLOCK=256, num_warps=4, num_stages=1)
        buf78 = extern_kernels.convolution(buf77, primals_80, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf79 = buf78
        del buf78
        buf80 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf79,
            primals_81, primals_82, buf80, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_81
        buf81 = extern_kernels.convolution(buf80, primals_83, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf81, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf82 = buf81
        del buf81
        buf83 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf82,
            primals_84, primals_85, buf83, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_84
        buf84 = extern_kernels.convolution(buf83, primals_86, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf84, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf85 = buf84
        del buf84
        triton_poi_fused_convolution_7[grid(8192)](buf85, primals_87, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_87
        buf86 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_8[grid(24576)](buf80, buf83, buf85, primals_88,
            buf86, 24576, XBLOCK=256, num_warps=4, num_stages=1)
        buf87 = extern_kernels.convolution(buf86, primals_89, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf88 = buf87
        del buf87
        buf89 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf88,
            primals_90, primals_91, buf89, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_90
        buf90 = extern_kernels.convolution(buf89, primals_92, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf90, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf91 = buf90
        del buf90
        buf92 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_6[grid(8192)](buf91,
            primals_93, primals_94, buf92, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_93
        buf93 = extern_kernels.convolution(buf92, primals_95, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf93, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf94 = buf93
        del buf93
        triton_poi_fused_convolution_7[grid(8192)](buf94, primals_96, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_96
        buf95 = empty_strided_cuda((4, 384, 4, 4), (6144, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_8[grid(24576)](buf89, buf92, buf94, primals_97,
            buf95, 24576, XBLOCK=256, num_warps=4, num_stages=1)
        buf96 = extern_kernels.convolution(buf95, primals_98, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf96, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf97 = buf96
        del buf96
        buf98 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_9[grid(32768)](buf97,
            primals_99, primals_100, buf98, 32768, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_99
        buf99 = extern_kernels.convolution(buf98, primals_101, stride=(1, 1
            ), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf99, (4, 4, 4, 4), (64, 16, 4, 1))
        buf100 = buf99
        del buf99
        triton_poi_fused_convolution_10[grid(256)](buf100, primals_102, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_102
    return (buf100, primals_3, primals_5, primals_6, primals_8, primals_9,
        primals_11, primals_12, primals_14, primals_15, primals_17,
        primals_18, primals_20, primals_21, primals_23, primals_24,
        primals_26, primals_27, primals_29, primals_30, primals_32,
        primals_33, primals_35, primals_36, primals_38, primals_39,
        primals_41, primals_42, primals_44, primals_45, primals_47,
        primals_48, primals_50, primals_51, primals_53, primals_55,
        primals_56, primals_58, primals_59, primals_61, primals_62,
        primals_64, primals_65, primals_67, primals_68, primals_70,
        primals_71, primals_73, primals_74, primals_76, primals_77,
        primals_79, primals_80, primals_82, primals_83, primals_85,
        primals_86, primals_88, primals_89, primals_91, primals_92,
        primals_94, primals_95, primals_97, primals_98, primals_100,
        primals_101, buf0, buf2, buf3, buf5, buf6, buf8, buf9, buf11, buf12,
        buf14, buf15, buf17, buf18, buf20, buf21, buf23, buf24, buf26,
        buf27, buf29, buf30, buf32, buf33, buf35, buf36, buf38, buf39,
        buf41, buf42, buf44, buf45, buf47, buf48, buf50, buf52, buf53,
        buf55, buf56, buf58, buf59, buf61, buf62, buf64, buf65, buf67,
        buf68, buf70, buf71, buf73, buf74, buf76, buf77, buf79, buf80,
        buf82, buf83, buf85, buf86, buf88, buf89, buf91, buf92, buf94,
        buf95, buf97, buf98)


class concatLayer(nn.Module):

    def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
        super(concatLayer, self).__init__()
        self.firstSub = self.concatLayerSub(in_channels,
            out_channels_perSub, '%d_stage%d_' % (i, j) + appendix + '_0')
        self.secondSub = self.concatLayerSub(out_channels_perSub,
            out_channels_perSub, '%d_stage%d_' % (i, j) + appendix + '_1')
        self.thirdSub = self.concatLayerSub(out_channels_perSub,
            out_channels_perSub, '%d_stage%d_' % (i, j) + appendix + '_2')

    def forward(self, x):
        firstSub = self.firstSub(x)
        secondSub = self.secondSub(firstSub)
        thirdSub = self.thirdSub(secondSub)
        out = torch.cat([firstSub, secondSub, thirdSub], 1)
        return out

    def concatLayerSub(self, in_channels, out_channels, layerName):
        concatLayerSubOrdered = OrderedDict()
        conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        concatLayerSubOrdered.update({('Mconv' + layerName): conv2d})
        concatLayerSubOrdered.update({('Mprelu' + layerName): nn.PReLU(
            out_channels)})
        return nn.Sequential(concatLayerSubOrdered)


class stage(nn.Module):

    def __init__(self, stageID, in_channels, out_channels_perSub,
        mid_channels, out_channels, appendix):
        super(stage, self).__init__()
        self.firstConcat = concatLayer(in_channels, out_channels_perSub, 1,
            stageID, appendix)
        self.secondConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 2, stageID, appendix)
        self.thirdConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 3, stageID, appendix)
        self.fourthConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 4, stageID, appendix)
        self.fifthConcat = concatLayer(3 * out_channels_perSub,
            out_channels_perSub, 5, stageID, appendix)
        conv2d = nn.Conv2d(3 * out_channels_perSub, mid_channels,
            kernel_size=1, padding=0)
        prelu = nn.PReLU(mid_channels)
        self.afterConcatsFirst = nn.Sequential(OrderedDict({(
            'Mconv6_stage%d_%s' % (stageID, appendix)): conv2d, (
            'Mprelu6_stage%d_%s' % (stageID, appendix)): prelu}))
        conv2d = nn.Conv2d(mid_channels, out_channels, kernel_size=1, padding=0
            )
        self.afterConcatsSecond = nn.Sequential(OrderedDict({(
            'Mconv7_stage%d_%s' % (stageID, appendix)): conv2d}))

    def forward(self, x):
        x = self.firstConcat(x)
        x = self.secondConcat(x)
        x = self.thirdConcat(x)
        x = self.fourthConcat(x)
        x = self.fifthConcat(x)
        x = self.afterConcatsFirst(x)
        out = self.afterConcatsSecond(x)
        return out


class L1PartNew(nn.Module):

    def __init__(self, in_channels, stage_out_channels):
        super(L1PartNew, self).__init__()
        self.firstStage = stage(0, in_channels, 96, 256, stage_out_channels,
            'L1')
        self.secondStage = stage(1, in_channels + stage_out_channels, 128, 
            512, stage_out_channels, 'L1')

    def forward(self, input_0, input_1):
        primals_3 = (self.firstStage.firstConcat.firstSub.
            Mconv1_stage0_L1_0.weight)
        primals_4 = (self.firstStage.firstConcat.firstSub.
            Mconv1_stage0_L1_0.bias)
        primals_5 = (self.firstStage.firstConcat.firstSub.
            Mprelu1_stage0_L1_0.weight)
        primals_6 = (self.firstStage.firstConcat.secondSub.
            Mconv1_stage0_L1_1.weight)
        primals_7 = (self.firstStage.firstConcat.secondSub.
            Mconv1_stage0_L1_1.bias)
        primals_8 = (self.firstStage.firstConcat.secondSub.
            Mprelu1_stage0_L1_1.weight)
        primals_9 = (self.firstStage.firstConcat.thirdSub.
            Mconv1_stage0_L1_2.weight)
        primals_10 = (self.firstStage.firstConcat.thirdSub.
            Mconv1_stage0_L1_2.bias)
        primals_11 = (self.firstStage.firstConcat.thirdSub.
            Mprelu1_stage0_L1_2.weight)
        primals_12 = (self.firstStage.secondConcat.firstSub.
            Mconv2_stage0_L1_0.weight)
        primals_13 = (self.firstStage.secondConcat.firstSub.
            Mconv2_stage0_L1_0.bias)
        primals_14 = (self.firstStage.secondConcat.firstSub.
            Mprelu2_stage0_L1_0.weight)
        primals_15 = (self.firstStage.secondConcat.secondSub.
            Mconv2_stage0_L1_1.weight)
        primals_16 = (self.firstStage.secondConcat.secondSub.
            Mconv2_stage0_L1_1.bias)
        primals_17 = (self.firstStage.secondConcat.secondSub.
            Mprelu2_stage0_L1_1.weight)
        primals_18 = (self.firstStage.secondConcat.thirdSub.
            Mconv2_stage0_L1_2.weight)
        primals_19 = (self.firstStage.secondConcat.thirdSub.
            Mconv2_stage0_L1_2.bias)
        primals_20 = (self.firstStage.secondConcat.thirdSub.
            Mprelu2_stage0_L1_2.weight)
        primals_21 = (self.firstStage.thirdConcat.firstSub.
            Mconv3_stage0_L1_0.weight)
        primals_22 = (self.firstStage.thirdConcat.firstSub.
            Mconv3_stage0_L1_0.bias)
        primals_23 = (self.firstStage.thirdConcat.firstSub.
            Mprelu3_stage0_L1_0.weight)
        primals_24 = (self.firstStage.thirdConcat.secondSub.
            Mconv3_stage0_L1_1.weight)
        primals_25 = (self.firstStage.thirdConcat.secondSub.
            Mconv3_stage0_L1_1.bias)
        primals_26 = (self.firstStage.thirdConcat.secondSub.
            Mprelu3_stage0_L1_1.weight)
        primals_27 = (self.firstStage.thirdConcat.thirdSub.
            Mconv3_stage0_L1_2.weight)
        primals_28 = (self.firstStage.thirdConcat.thirdSub.
            Mconv3_stage0_L1_2.bias)
        primals_29 = (self.firstStage.thirdConcat.thirdSub.
            Mprelu3_stage0_L1_2.weight)
        primals_30 = (self.firstStage.fourthConcat.firstSub.
            Mconv4_stage0_L1_0.weight)
        primals_31 = (self.firstStage.fourthConcat.firstSub.
            Mconv4_stage0_L1_0.bias)
        primals_32 = (self.firstStage.fourthConcat.firstSub.
            Mprelu4_stage0_L1_0.weight)
        primals_33 = (self.firstStage.fourthConcat.secondSub.
            Mconv4_stage0_L1_1.weight)
        primals_34 = (self.firstStage.fourthConcat.secondSub.
            Mconv4_stage0_L1_1.bias)
        primals_35 = (self.firstStage.fourthConcat.secondSub.
            Mprelu4_stage0_L1_1.weight)
        primals_36 = (self.firstStage.fourthConcat.thirdSub.
            Mconv4_stage0_L1_2.weight)
        primals_37 = (self.firstStage.fourthConcat.thirdSub.
            Mconv4_stage0_L1_2.bias)
        primals_38 = (self.firstStage.fourthConcat.thirdSub.
            Mprelu4_stage0_L1_2.weight)
        primals_39 = (self.firstStage.fifthConcat.firstSub.
            Mconv5_stage0_L1_0.weight)
        primals_40 = (self.firstStage.fifthConcat.firstSub.
            Mconv5_stage0_L1_0.bias)
        primals_41 = (self.firstStage.fifthConcat.firstSub.
            Mprelu5_stage0_L1_0.weight)
        primals_42 = (self.firstStage.fifthConcat.secondSub.
            Mconv5_stage0_L1_1.weight)
        primals_43 = (self.firstStage.fifthConcat.secondSub.
            Mconv5_stage0_L1_1.bias)
        primals_44 = (self.firstStage.fifthConcat.secondSub.
            Mprelu5_stage0_L1_1.weight)
        primals_45 = (self.firstStage.fifthConcat.thirdSub.
            Mconv5_stage0_L1_2.weight)
        primals_46 = (self.firstStage.fifthConcat.thirdSub.
            Mconv5_stage0_L1_2.bias)
        primals_47 = (self.firstStage.fifthConcat.thirdSub.
            Mprelu5_stage0_L1_2.weight)
        primals_48 = self.firstStage.afterConcatsFirst.Mconv6_stage0_L1.weight
        primals_49 = self.firstStage.afterConcatsFirst.Mconv6_stage0_L1.bias
        primals_50 = self.firstStage.afterConcatsFirst.Mprelu6_stage0_L1.weight
        primals_51 = self.firstStage.afterConcatsSecond.Mconv7_stage0_L1.weight
        primals_52 = self.firstStage.afterConcatsSecond.Mconv7_stage0_L1.bias
        primals_53 = (self.secondStage.firstConcat.firstSub.
            Mconv1_stage1_L1_0.weight)
        primals_54 = (self.secondStage.firstConcat.firstSub.
            Mconv1_stage1_L1_0.bias)
        primals_55 = (self.secondStage.firstConcat.firstSub.
            Mprelu1_stage1_L1_0.weight)
        primals_56 = (self.secondStage.firstConcat.secondSub.
            Mconv1_stage1_L1_1.weight)
        primals_57 = (self.secondStage.firstConcat.secondSub.
            Mconv1_stage1_L1_1.bias)
        primals_58 = (self.secondStage.firstConcat.secondSub.
            Mprelu1_stage1_L1_1.weight)
        primals_59 = (self.secondStage.firstConcat.thirdSub.
            Mconv1_stage1_L1_2.weight)
        primals_60 = (self.secondStage.firstConcat.thirdSub.
            Mconv1_stage1_L1_2.bias)
        primals_61 = (self.secondStage.firstConcat.thirdSub.
            Mprelu1_stage1_L1_2.weight)
        primals_62 = (self.secondStage.secondConcat.firstSub.
            Mconv2_stage1_L1_0.weight)
        primals_63 = (self.secondStage.secondConcat.firstSub.
            Mconv2_stage1_L1_0.bias)
        primals_64 = (self.secondStage.secondConcat.firstSub.
            Mprelu2_stage1_L1_0.weight)
        primals_65 = (self.secondStage.secondConcat.secondSub.
            Mconv2_stage1_L1_1.weight)
        primals_66 = (self.secondStage.secondConcat.secondSub.
            Mconv2_stage1_L1_1.bias)
        primals_67 = (self.secondStage.secondConcat.secondSub.
            Mprelu2_stage1_L1_1.weight)
        primals_68 = (self.secondStage.secondConcat.thirdSub.
            Mconv2_stage1_L1_2.weight)
        primals_69 = (self.secondStage.secondConcat.thirdSub.
            Mconv2_stage1_L1_2.bias)
        primals_70 = (self.secondStage.secondConcat.thirdSub.
            Mprelu2_stage1_L1_2.weight)
        primals_71 = (self.secondStage.thirdConcat.firstSub.
            Mconv3_stage1_L1_0.weight)
        primals_72 = (self.secondStage.thirdConcat.firstSub.
            Mconv3_stage1_L1_0.bias)
        primals_73 = (self.secondStage.thirdConcat.firstSub.
            Mprelu3_stage1_L1_0.weight)
        primals_74 = (self.secondStage.thirdConcat.secondSub.
            Mconv3_stage1_L1_1.weight)
        primals_75 = (self.secondStage.thirdConcat.secondSub.
            Mconv3_stage1_L1_1.bias)
        primals_76 = (self.secondStage.thirdConcat.secondSub.
            Mprelu3_stage1_L1_1.weight)
        primals_77 = (self.secondStage.thirdConcat.thirdSub.
            Mconv3_stage1_L1_2.weight)
        primals_78 = (self.secondStage.thirdConcat.thirdSub.
            Mconv3_stage1_L1_2.bias)
        primals_79 = (self.secondStage.thirdConcat.thirdSub.
            Mprelu3_stage1_L1_2.weight)
        primals_80 = (self.secondStage.fourthConcat.firstSub.
            Mconv4_stage1_L1_0.weight)
        primals_81 = (self.secondStage.fourthConcat.firstSub.
            Mconv4_stage1_L1_0.bias)
        primals_82 = (self.secondStage.fourthConcat.firstSub.
            Mprelu4_stage1_L1_0.weight)
        primals_83 = (self.secondStage.fourthConcat.secondSub.
            Mconv4_stage1_L1_1.weight)
        primals_84 = (self.secondStage.fourthConcat.secondSub.
            Mconv4_stage1_L1_1.bias)
        primals_85 = (self.secondStage.fourthConcat.secondSub.
            Mprelu4_stage1_L1_1.weight)
        primals_86 = (self.secondStage.fourthConcat.thirdSub.
            Mconv4_stage1_L1_2.weight)
        primals_87 = (self.secondStage.fourthConcat.thirdSub.
            Mconv4_stage1_L1_2.bias)
        primals_88 = (self.secondStage.fourthConcat.thirdSub.
            Mprelu4_stage1_L1_2.weight)
        primals_89 = (self.secondStage.fifthConcat.firstSub.
            Mconv5_stage1_L1_0.weight)
        primals_90 = (self.secondStage.fifthConcat.firstSub.
            Mconv5_stage1_L1_0.bias)
        primals_91 = (self.secondStage.fifthConcat.firstSub.
            Mprelu5_stage1_L1_0.weight)
        primals_92 = (self.secondStage.fifthConcat.secondSub.
            Mconv5_stage1_L1_1.weight)
        primals_93 = (self.secondStage.fifthConcat.secondSub.
            Mconv5_stage1_L1_1.bias)
        primals_94 = (self.secondStage.fifthConcat.secondSub.
            Mprelu5_stage1_L1_1.weight)
        primals_95 = (self.secondStage.fifthConcat.thirdSub.
            Mconv5_stage1_L1_2.weight)
        primals_96 = (self.secondStage.fifthConcat.thirdSub.
            Mconv5_stage1_L1_2.bias)
        primals_97 = (self.secondStage.fifthConcat.thirdSub.
            Mprelu5_stage1_L1_2.weight)
        primals_98 = self.secondStage.afterConcatsFirst.Mconv6_stage1_L1.weight
        primals_99 = self.secondStage.afterConcatsFirst.Mconv6_stage1_L1.bias
        primals_100 = (self.secondStage.afterConcatsFirst.Mprelu6_stage1_L1
            .weight)
        primals_101 = (self.secondStage.afterConcatsSecond.Mconv7_stage1_L1
            .weight)
        primals_102 = self.secondStage.afterConcatsSecond.Mconv7_stage1_L1.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53, primals_54,
            primals_55, primals_56, primals_57, primals_58, primals_59,
            primals_60, primals_61, primals_62, primals_63, primals_64,
            primals_65, primals_66, primals_67, primals_68, primals_69,
            primals_70, primals_71, primals_72, primals_73, primals_74,
            primals_75, primals_76, primals_77, primals_78, primals_79,
            primals_80, primals_81, primals_82, primals_83, primals_84,
            primals_85, primals_86, primals_87, primals_88, primals_89,
            primals_90, primals_91, primals_92, primals_93, primals_94,
            primals_95, primals_96, primals_97, primals_98, primals_99,
            primals_100, primals_101, primals_102])
        return output[0]
