# 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/td/ctdybbibnws4d7ukbk3fpn35zkgapxylowdhzwx7vgsllncbdrxa.py
# Topologically Sorted Source Nodes: [conv2d_1, branch3x3_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   branch3x3_1 => relu_1
#   conv2d_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_relu_0', '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_relu_0(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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tn/ctnpkytwfzoa42qs7orq2dukxazj6xkswsaijmba7yloy2im5ocs.py
# Topologically Sorted Source Nodes: [max_pool2d, branchM_1], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
# Source node to ATen node mapping:
#   branchM_1 => relu_5
#   max_pool2d => _low_memory_max_pool2d_with_offsets
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%primals_3, [3, 3], [1, 1], [1, 1], [1, 1], False), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_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_max_pool2d_with_indices_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_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
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x3 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5) + x3), tmp10 & xmask, other=float("-inf"))
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4) + x3), tmp16 & xmask, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-3) + x3), tmp23 & xmask, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-1) + x3), tmp30 & xmask, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x3), tmp33 & xmask, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x3), tmp36 & xmask, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + x3), tmp43 & xmask, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x3), tmp46 & xmask, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x3), tmp49 & xmask, other=float("-inf"))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tl.full([1], 0, tl.int32)
    tmp53 = triton_helpers.maximum(tmp52, tmp51)
    tl.store(in_out_ptr0 + (x3), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/py/cpyuiu2e7w6632is4nthozh6f4rhru6fh5gc65ctjkqznk5vbiro.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%relu, %relu_2, %relu_4, %relu_6], 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: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '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, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x2 = (xindex // 256)
    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 = tl.load(in_ptr1 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 8, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr2 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp15 & xmask, other=0.0)
    tmp17 = tl.load(in_ptr3 + ((-4) + x1), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp8, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp15, tmp19, tmp20)
    tmp22 = tmp0 >= tmp13
    tmp23 = tl.full([1], 12, tl.int64)
    tmp24 = tmp0 < tmp23
    tmp25 = tmp22 & tmp24
    tmp26 = tl.load(in_ptr4 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp25 & xmask, other=0.0)
    tmp27 = tl.load(in_ptr5 + ((-8) + x1), tmp25 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tmp26 + tmp27
    tmp29 = triton_helpers.maximum(tmp8, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp25, tmp29, tmp30)
    tmp32 = tmp0 >= tmp23
    tmp33 = tl.full([1], 16, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tl.load(in_ptr6 + (x0 + (16*((-12) + x1)) + (64*x2)), tmp32 & xmask, other=0.0)
    tmp36 = tl.load(in_ptr7 + ((-12) + x1), tmp32 & xmask, eviction_policy='evict_last', other=0.0)
    tmp37 = tmp35 + tmp36
    tmp38 = triton_helpers.maximum(tmp8, tmp37)
    tmp39 = tl.full(tmp38.shape, 0.0, tmp38.dtype)
    tmp40 = tl.where(tmp32, tmp38, tmp39)
    tmp41 = tl.where(tmp25, tmp31, tmp40)
    tmp42 = tl.where(tmp15, tmp21, tmp41)
    tmp43 = tl.where(tmp4, tmp11, tmp42)
    tl.store(out_ptr0 + (x3), tmp43, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/64/c64kbll2vwngzupaesjb3bkeqvvmff5z5c4ptcrwvfprjnzaxdkv.py
# Topologically Sorted Source Nodes: [conv2d_5, branchM_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   branchM_2 => relu_6
#   conv2d_5 => convolution_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_relu_threshold_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_3(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_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 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, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4, 5, 5), (100, 25, 5, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    assert_size_stride(primals_12, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_13, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), 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))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, branch3x3_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf2, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_6, 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))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(primals_3, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, branch5x5_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf5, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_10, stride=(1, 1), padding=(2, 2), 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [max_pool2d, branchM_1], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
        triton_poi_fused_max_pool2d_with_indices_relu_1.run(buf8, primals_3, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_12, stride=(1, 1), padding=(0, 0), 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 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf0, primals_2, buf3, primals_7, buf6, primals_11, buf9, primals_13, buf10, 1024, grid=grid(1024), stream=stream0)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_5, branchM_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_3.run(buf9, primals_13, buf11, 256, grid=grid(256), stream=stream0)
        del buf9
        del primals_13
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_4, branch5x5_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_3.run(buf6, primals_11, buf12, 256, grid=grid(256), stream=stream0)
        del buf6
        del primals_11
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_2, branch3x3_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_3.run(buf3, primals_7, buf13, 256, grid=grid(256), stream=stream0)
        del buf3
        del primals_7
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d, branch1x1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_3.run(buf0, primals_2, buf14, 256, grid=grid(256), stream=stream0)
        del buf0
        del primals_2
    return (buf10, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, buf2, buf5, buf8, buf11, buf12, buf13, buf14, )


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, 1, 1), (4, 1, 1, 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, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 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, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4, 5, 5), (100, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = 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])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn


class BasicConv2d(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride=1,
        padding=0, output_relu=True):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=
            kernel_size, stride=stride, padding=padding, bias=False)
        self.bn = nn.BatchNorm2d(out_planes)
        self.relu = nn.ReLU() if output_relu else None

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        if self.relu:
            x = self.relu(x)
        return x


class Inception(nn.Module):

    def __init__(self, channel, batch_norm=False):
        super(Inception, self).__init__()
        if batch_norm is False:
            self.branch1x1 = nn.Conv2d(channel[0], channel[1], kernel_size=
                (1, 1), stride=1)
            self.branch3x3_1 = nn.Conv2d(channel[0], channel[2],
                kernel_size=(1, 1), stride=1)
            self.branch3x3_2 = nn.Conv2d(channel[2], channel[3],
                kernel_size=(3, 3), stride=1, padding=1)
            self.branch5x5_1 = nn.Conv2d(channel[0], channel[4],
                kernel_size=(1, 1), stride=1)
            self.branch5x5_2 = nn.Conv2d(channel[4], channel[5],
                kernel_size=(5, 5), stride=1, padding=2)
            self.branchM_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
            self.branchM_2 = nn.Conv2d(channel[0], channel[6], kernel_size=
                (1, 1), stride=1)
        else:
            self.branch1x1 = BasicConv2d(channel[0], channel[1],
                kernel_size=(1, 1), stride=1)
            self.branch3x3_1 = BasicConv2d(channel[0], channel[2],
                kernel_size=(1, 1), stride=1)
            self.branch3x3_2 = BasicConv2d(channel[2], channel[3],
                kernel_size=(3, 3), stride=1, padding=1)
            self.branch5x5_1 = BasicConv2d(channel[0], channel[4],
                kernel_size=(1, 1), stride=1)
            self.branch5x5_2 = BasicConv2d(channel[4], channel[5],
                kernel_size=(5, 5), stride=1, padding=2)
            self.branchM_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
            self.branchM_2 = BasicConv2d(channel[0], channel[6],
                kernel_size=(1, 1), stride=1)
        self.relu = nn.ReLU(True)

    def forward(self, x):
        branch1x1 = self.relu(self.branch1x1(x))
        branch3x3_1 = self.relu(self.branch3x3_1(x))
        branch3x3_2 = self.relu(self.branch3x3_2(branch3x3_1))
        branch5x5_1 = self.relu(self.branch5x5_1(x))
        branch5x5_2 = self.relu(self.branch5x5_2(branch5x5_1))
        branchM_1 = self.relu(self.branchM_1(x))
        branchM_2 = self.relu(self.branchM_2(branchM_1))
        outputs = [branch1x1, branch3x3_2, branch5x5_2, branchM_2]
        return torch.cat(outputs, 1)


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


def get_init_inputs():
    return [[], {'channel': [4, 4, 4, 4, 4, 4, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_relu_0(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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_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
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x3 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5 + x3), tmp10 & xmask, other=float('-inf'))
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + x3), tmp16 & xmask, other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-3 + x3), tmp23 & xmask, other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-1 + x3), tmp30 & xmask, other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x3, tmp33 & xmask, other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x3), tmp36 & xmask, other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + x3), tmp43 & xmask, other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x3), tmp46 & xmask, other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x3), tmp49 & xmask, other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tl.full([1], 0, tl.int32)
    tmp53 = triton_helpers.maximum(tmp52, tmp51)
    tl.store(in_out_ptr0 + x3, tmp53, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    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 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 8, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr2 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp15 &
        xmask, other=0.0)
    tmp17 = tl.load(in_ptr3 + (-4 + x1), tmp15 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp8, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp15, tmp19, tmp20)
    tmp22 = tmp0 >= tmp13
    tmp23 = tl.full([1], 12, tl.int64)
    tmp24 = tmp0 < tmp23
    tmp25 = tmp22 & tmp24
    tmp26 = tl.load(in_ptr4 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp25 &
        xmask, other=0.0)
    tmp27 = tl.load(in_ptr5 + (-8 + x1), tmp25 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp28 = tmp26 + tmp27
    tmp29 = triton_helpers.maximum(tmp8, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp25, tmp29, tmp30)
    tmp32 = tmp0 >= tmp23
    tl.full([1], 16, tl.int64)
    tmp35 = tl.load(in_ptr6 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp32 &
        xmask, other=0.0)
    tmp36 = tl.load(in_ptr7 + (-12 + x1), tmp32 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp37 = tmp35 + tmp36
    tmp38 = triton_helpers.maximum(tmp8, tmp37)
    tmp39 = tl.full(tmp38.shape, 0.0, tmp38.dtype)
    tmp40 = tl.where(tmp32, tmp38, tmp39)
    tmp41 = tl.where(tmp25, tmp31, tmp40)
    tmp42 = tl.where(tmp15, tmp21, tmp41)
    tmp43 = tl.where(tmp4, tmp11, tmp42)
    tl.store(out_ptr0 + x3, tmp43, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_3(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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 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, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4, 5, 5), (100, 25, 5, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), 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 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(256)](buf2, primals_5, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf3 = extern_kernels.convolution(buf2, primals_6, 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 = extern_kernels.convolution(primals_3, primals_8, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_0[grid(256)](buf5, primals_9, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf6 = extern_kernels.convolution(buf5, primals_10, stride=(1, 1),
            padding=(2, 2), 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf8 = buf7
        del buf7
        triton_poi_fused_max_pool2d_with_indices_relu_1[grid(256)](buf8,
            primals_3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_12, stride=(1, 1),
            padding=(0, 0), 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 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_2[grid(1024)](buf0, primals_2, buf3, primals_7,
            buf6, primals_11, buf9, primals_13, buf10, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(256)](buf9,
            primals_13, buf11, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf9
        del primals_13
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(256)](buf6,
            primals_11, buf12, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf6
        del primals_11
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(256)](buf3,
            primals_7, buf13, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_7
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(256)](buf0,
            primals_2, buf14, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return (buf10, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, buf2, buf5, buf8, buf11, buf12, buf13, buf14)


class BasicConv2d(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride=1,
        padding=0, output_relu=True):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=
            kernel_size, stride=stride, padding=padding, bias=False)
        self.bn = nn.BatchNorm2d(out_planes)
        self.relu = nn.ReLU() if output_relu else None

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        if self.relu:
            x = self.relu(x)
        return x


class InceptionNew(nn.Module):

    def __init__(self, channel, batch_norm=False):
        super(InceptionNew, self).__init__()
        if batch_norm is False:
            self.branch1x1 = nn.Conv2d(channel[0], channel[1], kernel_size=
                (1, 1), stride=1)
            self.branch3x3_1 = nn.Conv2d(channel[0], channel[2],
                kernel_size=(1, 1), stride=1)
            self.branch3x3_2 = nn.Conv2d(channel[2], channel[3],
                kernel_size=(3, 3), stride=1, padding=1)
            self.branch5x5_1 = nn.Conv2d(channel[0], channel[4],
                kernel_size=(1, 1), stride=1)
            self.branch5x5_2 = nn.Conv2d(channel[4], channel[5],
                kernel_size=(5, 5), stride=1, padding=2)
            self.branchM_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
            self.branchM_2 = nn.Conv2d(channel[0], channel[6], kernel_size=
                (1, 1), stride=1)
        else:
            self.branch1x1 = BasicConv2d(channel[0], channel[1],
                kernel_size=(1, 1), stride=1)
            self.branch3x3_1 = BasicConv2d(channel[0], channel[2],
                kernel_size=(1, 1), stride=1)
            self.branch3x3_2 = BasicConv2d(channel[2], channel[3],
                kernel_size=(3, 3), stride=1, padding=1)
            self.branch5x5_1 = BasicConv2d(channel[0], channel[4],
                kernel_size=(1, 1), stride=1)
            self.branch5x5_2 = BasicConv2d(channel[4], channel[5],
                kernel_size=(5, 5), stride=1, padding=2)
            self.branchM_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
            self.branchM_2 = BasicConv2d(channel[0], channel[6],
                kernel_size=(1, 1), stride=1)
        self.relu = nn.ReLU(True)

    def forward(self, input_0):
        primals_1 = self.branch1x1.weight
        primals_2 = self.branch1x1.bias
        primals_4 = self.branch3x3_1.weight
        primals_5 = self.branch3x3_1.bias
        primals_6 = self.branch3x3_2.weight
        primals_7 = self.branch3x3_2.bias
        primals_8 = self.branch5x5_1.weight
        primals_9 = self.branch5x5_1.bias
        primals_10 = self.branch5x5_2.weight
        primals_11 = self.branch5x5_2.bias
        primals_12 = self.branchM_2.weight
        primals_13 = self.branchM_2.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])
        return output[0]
