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

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


# kernel path: runs/run_shard_4/inductor_cache/sz/cszryrj5kmfztyrg6r3zq6t6rqkltip5llc24nn3ozvittnostjb.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=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_0 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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: '*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_0', '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_0(in_out_ptr0, 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
    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')
    tmp5 = tl.load(in_ptr1 + (x1), xmask, 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, xmask)
    tl.store(out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/32/c32v7egt4mupqssam3gmac2qgv3ujprjybthsgweflmot256qqw7.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_4 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_8, %primals_9, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_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=[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_1', '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_1(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')


# kernel path: runs/run_shard_4/inductor_cache/yc/cyctw7fzbq3yqgryx4i6waopqgcuvh2eufmovqoakos4fawmbbyw.py
# Topologically Sorted Source Nodes: [x_cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_cat_1 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where, %where_1, %where_2], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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=[1024], 
    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_2', '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_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 12
    x0 = xindex % 16
    x2 = (xindex // 192)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp11 & xmask, other=0.0)
    tmp15 = 0.0
    tmp16 = tmp14 > tmp15
    tmp17 = tl.load(in_ptr3 + ((-8) + x1), tmp11 & xmask, 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, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, ), (1, ))
    assert_size_stride(primals_14, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_15, (4, ), (1, ))
    assert_size_stride(primals_16, (4, ), (1, ))
    assert_size_stride(primals_17, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_18, (4, ), (1, ))
    assert_size_stride(primals_19, (4, ), (1, ))
    assert_size_stride(primals_20, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_21, (4, ), (1, ))
    assert_size_stride(primals_22, (4, ), (1, ))
    assert_size_stride(primals_23, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_24, (4, ), (1, ))
    assert_size_stride(primals_25, (4, ), (1, ))
    assert_size_stride(primals_26, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_27, (4, ), (1, ))
    assert_size_stride(primals_28, (4, ), (1, ))
    assert_size_stride(primals_29, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_30, (4, ), (1, ))
    assert_size_stride(primals_31, (4, ), (1, ))
    assert_size_stride(primals_32, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_33, (4, ), (1, ))
    assert_size_stride(primals_34, (4, ), (1, ))
    assert_size_stride(primals_35, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_36, (4, ), (1, ))
    assert_size_stride(primals_37, (4, ), (1, ))
    assert_size_stride(primals_38, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_39, (4, ), (1, ))
    assert_size_stride(primals_40, (4, ), (1, ))
    assert_size_stride(primals_41, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_42, (4, ), (1, ))
    assert_size_stride(primals_43, (4, ), (1, ))
    assert_size_stride(primals_44, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_45, (4, ), (1, ))
    assert_size_stride(primals_46, (4, ), (1, ))
    assert_size_stride(primals_47, (4, 12, 1, 1), (12, 1, 1, 1))
    assert_size_stride(primals_48, (4, ), (1, ))
    assert_size_stride(primals_49, (4, ), (1, ))
    assert_size_stride(primals_50, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_51, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten._prelu_kernel]
        stream0 = get_raw_stream(0)
        triton_poi_fused__prelu_kernel_convolution_0.run(buf1, primals_2, primals_4, buf2, 256, grid=grid(256), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf4, primals_6, primals_7, buf5, 256, grid=grid(256), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf7, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf8 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_cat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf2, buf5, buf7, primals_10, buf8, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
        buf10 = buf9; del buf9  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf10, primals_12, primals_13, buf11, 256, grid=grid(256), stream=stream0)
        del primals_12
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
        buf13 = buf12; del buf12  # reuse
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf13, primals_15, primals_16, buf14, 256, grid=grid(256), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, primals_17, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 4, 4, 4), (64, 16, 4, 1))
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf16, primals_18, 256, grid=grid(256), stream=stream0)
        del primals_18
        buf17 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_cat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf11, buf14, buf16, primals_19, buf17, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 4, 4, 4), (64, 16, 4, 1))
        buf19 = buf18; del buf18  # reuse
        buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_12, x_13], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf19, primals_21, primals_22, buf20, 256, grid=grid(256), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.convolution]
        buf21 = extern_kernels.convolution(buf20, primals_23, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 4, 4, 4), (64, 16, 4, 1))
        buf22 = buf21; del buf21  # reuse
        buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_14, x_15], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf22, primals_24, primals_25, buf23, 256, grid=grid(256), stream=stream0)
        del primals_24
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 4, 4, 4), (64, 16, 4, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf25, primals_27, 256, grid=grid(256), stream=stream0)
        del primals_27
        buf26 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_cat_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf20, buf23, buf25, primals_28, buf26, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_18], Original ATen: [aten.convolution]
        buf27 = extern_kernels.convolution(buf26, primals_29, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 4, 4, 4), (64, 16, 4, 1))
        buf28 = buf27; del buf27  # reuse
        buf29 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_18, x_19], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf28, primals_30, primals_31, buf29, 256, grid=grid(256), stream=stream0)
        del primals_30
        # Topologically Sorted Source Nodes: [x_20], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, primals_32, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 4, 4, 4), (64, 16, 4, 1))
        buf31 = buf30; del buf30  # reuse
        buf32 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_20, x_21], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf31, primals_33, primals_34, buf32, 256, grid=grid(256), stream=stream0)
        del primals_33
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.convolution]
        buf33 = extern_kernels.convolution(buf32, primals_35, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 4, 4, 4), (64, 16, 4, 1))
        buf34 = buf33; del buf33  # reuse
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf34, primals_36, 256, grid=grid(256), stream=stream0)
        del primals_36
        buf35 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_cat_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf29, buf32, buf34, primals_37, buf35, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_24], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 4, 4, 4), (64, 16, 4, 1))
        buf37 = buf36; del buf36  # reuse
        buf38 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_24, x_25], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf37, primals_39, primals_40, buf38, 256, grid=grid(256), stream=stream0)
        del primals_39
        # Topologically Sorted Source Nodes: [x_26], Original ATen: [aten.convolution]
        buf39 = extern_kernels.convolution(buf38, primals_41, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 4, 4, 4), (64, 16, 4, 1))
        buf40 = buf39; del buf39  # reuse
        buf41 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_26, x_27], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf40, primals_42, primals_43, buf41, 256, grid=grid(256), stream=stream0)
        del primals_42
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.convolution]
        buf42 = extern_kernels.convolution(buf41, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 4, 4, 4), (64, 16, 4, 1))
        buf43 = buf42; del buf42  # reuse
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf43, primals_45, 256, grid=grid(256), stream=stream0)
        del primals_45
        buf44 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_cat_5], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf38, buf41, buf43, primals_46, buf44, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_30], Original ATen: [aten.convolution]
        buf45 = extern_kernels.convolution(buf44, primals_47, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf45, (4, 4, 4, 4), (64, 16, 4, 1))
        buf46 = buf45; del buf45  # reuse
        buf47 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_30, x_31], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_0.run(buf46, primals_48, primals_49, buf47, 256, grid=grid(256), stream=stream0)
        del primals_48
        # Topologically Sorted Source Nodes: [stage_output], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, primals_50, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 4, 4, 4), (64, 16, 4, 1))
        buf49 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [stage_output], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf49, primals_51, 256, grid=grid(256), stream=stream0)
        del primals_51
    return (buf49, primals_1, primals_3, primals_4, primals_5, primals_7, primals_8, primals_10, primals_11, primals_13, primals_14, primals_16, primals_17, primals_19, primals_20, primals_22, primals_23, primals_25, primals_26, primals_28, primals_29, primals_31, primals_32, primals_34, primals_35, primals_37, primals_38, primals_40, primals_41, primals_43, primals_44, primals_46, primals_47, primals_49, primals_50, buf1, buf2, buf4, buf5, buf7, buf8, buf10, buf11, buf13, buf14, buf16, buf17, buf19, buf20, buf22, buf23, buf25, buf26, buf28, buf29, buf31, buf32, buf34, buf35, buf37, buf38, buf40, buf41, buf43, buf44, buf46, buf47, )


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, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 12, 3, 3), (108, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((4, 12, 3, 3), (108, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((4, 12, 3, 3), (108, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((4, 12, 3, 3), (108, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((4, 12, 1, 1), (12, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = 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])
    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.onnx
import torch
import torch.nn as nn
import torch.utils.data


class ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
        padding=1):
        super(ConvBlock, self).__init__()
        self.Mconv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=stride, padding=
            padding)
        self.MPrelu = nn.PReLU(num_parameters=out_channels)

    def forward(self, x):
        x = self.Mconv(x)
        x = self.MPrelu(x)
        return x


class StageBlock(nn.Module):
    """ L1/L2 StageBlock Template """

    def __init__(self, in_channels, inner_channels, innerout_channels,
        out_channels):
        super(StageBlock, self).__init__()
        self.Mconv1_0 = ConvBlock(in_channels, inner_channels)
        self.Mconv1_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv1_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv2_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv2_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv2_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv3_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv3_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv3_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv4_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv4_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv4_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv5_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv5_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv5_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv6 = ConvBlock(inner_channels * 3, innerout_channels,
            kernel_size=1, stride=1, padding=0)
        self.Mconv7 = nn.Conv2d(in_channels=innerout_channels, out_channels
            =out_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        out1_1 = self.Mconv1_0(x)
        out2_1 = self.Mconv1_1(out1_1)
        out3_1 = self.Mconv1_2(out2_1)
        x_cat_1 = torch.cat([out1_1, out2_1, out3_1], 1)
        out1_2 = self.Mconv2_0(x_cat_1)
        out2_2 = self.Mconv2_1(out1_2)
        out3_2 = self.Mconv2_2(out2_2)
        x_cat_2 = torch.cat([out1_2, out2_2, out3_2], 1)
        out1_3 = self.Mconv3_0(x_cat_2)
        out2_3 = self.Mconv3_1(out1_3)
        out3_3 = self.Mconv3_2(out2_3)
        x_cat_3 = torch.cat([out1_3, out2_3, out3_3], 1)
        out1_4 = self.Mconv4_0(x_cat_3)
        out2_4 = self.Mconv4_1(out1_4)
        out3_4 = self.Mconv4_2(out2_4)
        x_cat_4 = torch.cat([out1_4, out2_4, out3_4], 1)
        out1_5 = self.Mconv5_0(x_cat_4)
        out2_5 = self.Mconv5_1(out1_5)
        out3_5 = self.Mconv5_2(out2_5)
        x_cat_5 = torch.cat([out1_5, out2_5, out3_5], 1)
        out_6 = self.Mconv6(x_cat_5)
        stage_output = self.Mconv7(out_6)
        return stage_output


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'inner_channels': 4, 'innerout_channels':
        4, '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.onnx
import torch
import torch.nn as nn
import torch.utils.data
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__prelu_kernel_convolution_0(in_out_ptr0, 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
    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')
    tmp5 = tl.load(in_ptr1 + x1, xmask, 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, xmask)
    tl.store(out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_1(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)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp11 &
        xmask, other=0.0)
    tmp15 = 0.0
    tmp16 = tmp14 > tmp15
    tmp17 = tl.load(in_ptr3 + (-8 + x1), tmp11 & xmask, 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, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_18, (4,), (1,))
    assert_size_stride(primals_19, (4,), (1,))
    assert_size_stride(primals_20, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_21, (4,), (1,))
    assert_size_stride(primals_22, (4,), (1,))
    assert_size_stride(primals_23, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_24, (4,), (1,))
    assert_size_stride(primals_25, (4,), (1,))
    assert_size_stride(primals_26, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_27, (4,), (1,))
    assert_size_stride(primals_28, (4,), (1,))
    assert_size_stride(primals_29, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_30, (4,), (1,))
    assert_size_stride(primals_31, (4,), (1,))
    assert_size_stride(primals_32, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_33, (4,), (1,))
    assert_size_stride(primals_34, (4,), (1,))
    assert_size_stride(primals_35, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_36, (4,), (1,))
    assert_size_stride(primals_37, (4,), (1,))
    assert_size_stride(primals_38, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_39, (4,), (1,))
    assert_size_stride(primals_40, (4,), (1,))
    assert_size_stride(primals_41, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_42, (4,), (1,))
    assert_size_stride(primals_43, (4,), (1,))
    assert_size_stride(primals_44, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_45, (4,), (1,))
    assert_size_stride(primals_46, (4,), (1,))
    assert_size_stride(primals_47, (4, 12, 1, 1), (12, 1, 1, 1))
    assert_size_stride(primals_48, (4,), (1,))
    assert_size_stride(primals_49, (4,), (1,))
    assert_size_stride(primals_50, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_51, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf1,
            primals_2, primals_4, buf2, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf4,
            primals_6, primals_7, buf5, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_6
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_1[grid(256)](buf7, primals_9, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_2[grid(768)](buf2, buf5, buf7, primals_10,
            buf8, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf10,
            primals_12, primals_13, buf11, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_12
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
        buf13 = buf12
        del buf12
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf13,
            primals_15, primals_16, buf14, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_15
        buf15 = extern_kernels.convolution(buf14, primals_17, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 4, 4, 4), (64, 16, 4, 1))
        buf16 = buf15
        del buf15
        triton_poi_fused_convolution_1[grid(256)](buf16, primals_18, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_18
        buf17 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_2[grid(768)](buf11, buf14, buf16, primals_19,
            buf17, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf18 = extern_kernels.convolution(buf17, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 4, 4, 4), (64, 16, 4, 1))
        buf19 = buf18
        del buf18
        buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf19,
            primals_21, primals_22, buf20, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_21
        buf21 = extern_kernels.convolution(buf20, primals_23, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 4, 4, 4), (64, 16, 4, 1))
        buf22 = buf21
        del buf21
        buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf22,
            primals_24, primals_25, buf23, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_24
        buf24 = extern_kernels.convolution(buf23, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 4, 4, 4), (64, 16, 4, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_1[grid(256)](buf25, primals_27, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_27
        buf26 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_2[grid(768)](buf20, buf23, buf25, primals_28,
            buf26, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf27 = extern_kernels.convolution(buf26, primals_29, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 4, 4, 4), (64, 16, 4, 1))
        buf28 = buf27
        del buf27
        buf29 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf28,
            primals_30, primals_31, buf29, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_30
        buf30 = extern_kernels.convolution(buf29, primals_32, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 4, 4, 4), (64, 16, 4, 1))
        buf31 = buf30
        del buf30
        buf32 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf31,
            primals_33, primals_34, buf32, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_33
        buf33 = extern_kernels.convolution(buf32, primals_35, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 4, 4, 4), (64, 16, 4, 1))
        buf34 = buf33
        del buf33
        triton_poi_fused_convolution_1[grid(256)](buf34, primals_36, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_36
        buf35 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_2[grid(768)](buf29, buf32, buf34, primals_37,
            buf35, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf36 = extern_kernels.convolution(buf35, primals_38, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 4, 4, 4), (64, 16, 4, 1))
        buf37 = buf36
        del buf36
        buf38 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf37,
            primals_39, primals_40, buf38, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_39
        buf39 = extern_kernels.convolution(buf38, primals_41, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 4, 4, 4), (64, 16, 4, 1))
        buf40 = buf39
        del buf39
        buf41 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf40,
            primals_42, primals_43, buf41, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_42
        buf42 = extern_kernels.convolution(buf41, primals_44, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 4, 4, 4), (64, 16, 4, 1))
        buf43 = buf42
        del buf42
        triton_poi_fused_convolution_1[grid(256)](buf43, primals_45, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_45
        buf44 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_2[grid(768)](buf38, buf41, buf43, primals_46,
            buf44, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf45 = extern_kernels.convolution(buf44, primals_47, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf45, (4, 4, 4, 4), (64, 16, 4, 1))
        buf46 = buf45
        del buf45
        buf47 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf46,
            primals_48, primals_49, buf47, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_48
        buf48 = extern_kernels.convolution(buf47, primals_50, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 4, 4, 4), (64, 16, 4, 1))
        buf49 = buf48
        del buf48
        triton_poi_fused_convolution_1[grid(256)](buf49, primals_51, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_51
    return (buf49, primals_1, primals_3, primals_4, primals_5, primals_7,
        primals_8, primals_10, primals_11, primals_13, primals_14,
        primals_16, primals_17, primals_19, primals_20, primals_22,
        primals_23, primals_25, primals_26, primals_28, primals_29,
        primals_31, primals_32, primals_34, primals_35, primals_37,
        primals_38, primals_40, primals_41, primals_43, primals_44,
        primals_46, primals_47, primals_49, primals_50, buf1, buf2, buf4,
        buf5, buf7, buf8, buf10, buf11, buf13, buf14, buf16, buf17, buf19,
        buf20, buf22, buf23, buf25, buf26, buf28, buf29, buf31, buf32,
        buf34, buf35, buf37, buf38, buf40, buf41, buf43, buf44, buf46, buf47)


class ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
        padding=1):
        super(ConvBlock, self).__init__()
        self.Mconv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=stride, padding=
            padding)
        self.MPrelu = nn.PReLU(num_parameters=out_channels)

    def forward(self, x):
        x = self.Mconv(x)
        x = self.MPrelu(x)
        return x


class StageBlockNew(nn.Module):
    """ L1/L2 StageBlock Template """

    def __init__(self, in_channels, inner_channels, innerout_channels,
        out_channels):
        super(StageBlockNew, self).__init__()
        self.Mconv1_0 = ConvBlock(in_channels, inner_channels)
        self.Mconv1_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv1_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv2_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv2_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv2_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv3_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv3_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv3_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv4_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv4_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv4_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv5_0 = ConvBlock(inner_channels * 3, inner_channels)
        self.Mconv5_1 = ConvBlock(inner_channels, inner_channels)
        self.Mconv5_2 = ConvBlock(inner_channels, inner_channels)
        self.Mconv6 = ConvBlock(inner_channels * 3, innerout_channels,
            kernel_size=1, stride=1, padding=0)
        self.Mconv7 = nn.Conv2d(in_channels=innerout_channels, out_channels
            =out_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, input_0):
        primals_1 = self.Mconv1_0.Mconv.weight
        primals_2 = self.Mconv1_0.Mconv.bias
        primals_4 = self.Mconv1_0.MPrelu.weight
        primals_5 = self.Mconv1_1.Mconv.weight
        primals_6 = self.Mconv1_1.Mconv.bias
        primals_7 = self.Mconv1_1.MPrelu.weight
        primals_8 = self.Mconv1_2.Mconv.weight
        primals_9 = self.Mconv1_2.Mconv.bias
        primals_10 = self.Mconv1_2.MPrelu.weight
        primals_11 = self.Mconv2_0.Mconv.weight
        primals_12 = self.Mconv2_0.Mconv.bias
        primals_13 = self.Mconv2_0.MPrelu.weight
        primals_14 = self.Mconv2_1.Mconv.weight
        primals_15 = self.Mconv2_1.Mconv.bias
        primals_16 = self.Mconv2_1.MPrelu.weight
        primals_17 = self.Mconv2_2.Mconv.weight
        primals_18 = self.Mconv2_2.Mconv.bias
        primals_19 = self.Mconv2_2.MPrelu.weight
        primals_20 = self.Mconv3_0.Mconv.weight
        primals_21 = self.Mconv3_0.Mconv.bias
        primals_22 = self.Mconv3_0.MPrelu.weight
        primals_23 = self.Mconv3_1.Mconv.weight
        primals_24 = self.Mconv3_1.Mconv.bias
        primals_25 = self.Mconv3_1.MPrelu.weight
        primals_26 = self.Mconv3_2.Mconv.weight
        primals_27 = self.Mconv3_2.Mconv.bias
        primals_28 = self.Mconv3_2.MPrelu.weight
        primals_29 = self.Mconv4_0.Mconv.weight
        primals_30 = self.Mconv4_0.Mconv.bias
        primals_31 = self.Mconv4_0.MPrelu.weight
        primals_32 = self.Mconv4_1.Mconv.weight
        primals_33 = self.Mconv4_1.Mconv.bias
        primals_34 = self.Mconv4_1.MPrelu.weight
        primals_35 = self.Mconv4_2.Mconv.weight
        primals_36 = self.Mconv4_2.Mconv.bias
        primals_37 = self.Mconv4_2.MPrelu.weight
        primals_38 = self.Mconv5_0.Mconv.weight
        primals_39 = self.Mconv5_0.Mconv.bias
        primals_40 = self.Mconv5_0.MPrelu.weight
        primals_41 = self.Mconv5_1.Mconv.weight
        primals_42 = self.Mconv5_1.Mconv.bias
        primals_43 = self.Mconv5_1.MPrelu.weight
        primals_44 = self.Mconv5_2.Mconv.weight
        primals_45 = self.Mconv5_2.Mconv.bias
        primals_46 = self.Mconv5_2.MPrelu.weight
        primals_47 = self.Mconv6.Mconv.weight
        primals_48 = self.Mconv6.Mconv.bias
        primals_49 = self.Mconv6.MPrelu.weight
        primals_50 = self.Mconv7.weight
        primals_51 = self.Mconv7.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            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])
        return output[0]
