# 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/jg/cjg4n7hh7cupzyz2emalse7yildcxyj3fuy6qkeizekhq7icei2w.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_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=[65536], 
    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_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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3844) % 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/in/cins5c6zlnq7pgm4vfwzi3as4r7oax7lsq5fe72bugshq22tmtn4.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.max_pool2d_with_indices, aten.leaky_relu]
# Source node to ATen node mapping:
#   out_1 => _low_memory_max_pool2d_with_offsets, getitem_1
#   out_2 => gt, mul, where
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%convolution, [3, 3], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%getitem, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem, 0.01), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %getitem, %mul), kwargs = {})
triton_poi_fused_leaky_relu_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_leaky_relu_max_pool2d_with_indices_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: '*i1', 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_leaky_relu_max_pool2d_with_indices_1', 'mutated_arg_names': [], '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_leaky_relu_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 30
    x1 = (xindex // 30) % 30
    x5 = (xindex // 900)
    x3 = (xindex // 3600)
    x4 = xindex % 3600
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (62 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (63 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (64 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (124 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (125 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (126 + (2*x0) + (124*x1) + (3844*x5)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tmp42 = 0.0
    tmp43 = tmp16 > tmp42
    tmp44 = 0.01
    tmp45 = tmp16 * tmp44
    tmp46 = tl.where(tmp43, tmp16, tmp45)
    tl.store(out_ptr0 + (x4 + (3616*x3)), tmp16, xmask)
    tl.store(out_ptr1 + (x4 + (3712*x3)), tmp41, xmask)
    tl.store(out_ptr2 + (x6), tmp43, xmask)
    tl.store(out_ptr3 + (x6), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zn/cznwpygb74loongtcbmxcox74v3hegtd2heru64iqr6vyvjyi7yn.py
# Topologically Sorted Source Nodes: [out_3, out_4], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   out_3 => convolution_1
#   out_4 => gt_1, mul_1, where_1
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.01), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
triton_poi_fused_convolution_leaky_relu_2 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_2', '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_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 900) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2t/c2t674xamuewg7ymye7mb5677ok4cfcc2tby52qvfbqaahof2mix.py
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   out_5 => convolution_2
#   out_6 => add
#   out_7 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %getitem), kwargs = {})
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.01), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %add, %mul_2), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_3 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 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_add_convolution_leaky_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 900) % 4
    x2 = (xindex // 3600)
    x4 = xindex % 3600
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + (3616*x2)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
    tl.store(out_ptr1 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/r2/cr2ztc3verfb34lizjo7ov4g3x2g3fhwgo7yr4vauiraxvwuh2h6.py
# Topologically Sorted Source Nodes: [out_5, out_6, out_10, out_11], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   out_10 => convolution_4
#   out_11 => add_1
#   out_5 => convolution_2
#   out_6 => add
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %getitem), kwargs = {})
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_4, %add), kwargs = {})
triton_poi_fused_add_convolution_4 = async_compile.triton('triton_poi_fused_add_convolution_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*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_add_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 900) % 4
    x2 = (xindex // 3600)
    x4 = xindex % 3600
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), xmask)
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x4 + (3616*x2)), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 + tmp7
    tl.store(in_out_ptr0 + (x3), tmp8, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 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, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, 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, 62, 62), (15376, 3844, 62, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_3, 61504, grid=grid(61504), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 30, 30), (3616, 900, 30, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 30, 30), (3712, 900, 30, 1), torch.int8)
        buf4 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.max_pool2d_with_indices, aten.leaky_relu]
        triton_poi_fused_leaky_relu_max_pool2d_with_indices_1.run(buf1, buf2, buf3, buf4, buf5, 14400, grid=grid(14400), stream=stream0)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_4, 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, 30, 30), (3600, 900, 30, 1))
        buf7 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3, out_4], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf6, primals_5, buf7, buf8, 14400, grid=grid(14400), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_6, 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, 30, 30), (3600, 900, 30, 1))
        buf10 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch.bool)
        buf11 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
        triton_poi_fused_add_convolution_leaky_relu_3.run(buf9, primals_7, buf2, buf10, buf11, 14400, grid=grid(14400), stream=stream0)
        # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_8, 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, 30, 30), (3600, 900, 30, 1))
        buf13 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch.bool)
        buf14 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf12, primals_9, buf13, buf14, 14400, grid=grid(14400), stream=stream0)
        del buf12
        del primals_9
        # Topologically Sorted Source Nodes: [out_10], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, primals_10, 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, 30, 30), (3600, 900, 30, 1))
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [out_5, out_6, out_10, out_11], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_4.run(buf16, primals_11, buf9, primals_7, buf2, 14400, grid=grid(14400), stream=stream0)
        del buf2
        del buf9
        del primals_11
        del primals_7
    return (buf16, primals_1, primals_2, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf4, buf5, buf7, buf8, buf10, buf11, 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, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 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, 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, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = 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])
    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


class ResBlock(torch.nn.Module):

    def __init__(self, num_channel):
        super(ResBlock, self).__init__()
        self.conv1 = torch.nn.Conv2d(num_channel, num_channel, kernel_size=
            3, stride=1, padding=1)
        self.conv2 = torch.nn.Conv2d(num_channel, num_channel, kernel_size=
            3, stride=1, padding=1)
        self.leaky_relu = torch.nn.LeakyReLU()

    def forward(self, x):
        out = x
        out = self.leaky_relu(out)
        out = self.conv1(out)
        out = self.leaky_relu(out)
        out = self.conv2(out)
        return out + x


class ConvBlock(torch.nn.Module):

    def __init__(self, in_channel, num_channel):
        super(ConvBlock, self).__init__()
        self.conv = torch.nn.Conv2d(in_channel, num_channel, kernel_size=3,
            stride=1)
        self.max = torch.nn.MaxPool2d(kernel_size=3, stride=2)
        self.res1 = ResBlock(num_channel=num_channel)
        self.res2 = ResBlock(num_channel=num_channel)
        self.leaky_relu = torch.nn.LeakyReLU()

    def forward(self, x):
        out = x
        out = self.conv(out)
        out = self.max(out)
        out = self.res1(out)
        out = self.res2(out)
        return out


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


def get_init_inputs():
    return [[], {'in_channel': 4, 'num_channel': 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
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 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_leaky_relu_max_pool2d_with_indices_1(in_ptr0, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 30
    x1 = xindex // 30 % 30
    x5 = xindex // 900
    x3 = xindex // 3600
    x4 = xindex % 3600
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (62 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (63 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (64 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (124 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (125 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (126 + 2 * x0 + 124 * x1 + 3844 * x5), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tmp42 = 0.0
    tmp43 = tmp16 > tmp42
    tmp44 = 0.01
    tmp45 = tmp16 * tmp44
    tmp46 = tl.where(tmp43, tmp16, tmp45)
    tl.store(out_ptr0 + (x4 + 3616 * x3), tmp16, xmask)
    tl.store(out_ptr1 + (x4 + 3712 * x3), tmp41, xmask)
    tl.store(out_ptr2 + x6, tmp43, xmask)
    tl.store(out_ptr3 + x6, tmp46, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 900 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_3(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 900 % 4
    x2 = xindex // 3600
    x4 = xindex % 3600
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + 3616 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 900 % 4
    x2 = xindex // 3600
    x4 = xindex % 3600
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x4 + 3616 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 + tmp7
    tl.store(in_out_ptr0 + x3, tmp8, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 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, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, 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, 62, 62), (15376, 3844, 62, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(61504)](buf1, primals_3, 61504,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 30, 30), (3616, 900, 30, 1), torch
            .float32)
        buf3 = empty_strided_cuda((4, 4, 30, 30), (3712, 900, 30, 1), torch
            .int8)
        buf4 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch
            .bool)
        buf5 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch
            .float32)
        triton_poi_fused_leaky_relu_max_pool2d_with_indices_1[grid(14400)](buf1
            , buf2, buf3, buf4, buf5, 14400, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_4, 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, 30, 30), (3600, 900, 30, 1))
        buf7 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch
            .bool)
        buf8 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_2[grid(14400)](buf6,
            primals_5, buf7, buf8, 14400, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_5
        buf9 = extern_kernels.convolution(buf8, primals_6, 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, 30, 30), (3600, 900, 30, 1))
        buf10 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1),
            torch.bool)
        buf11 = buf6
        del buf6
        triton_poi_fused_add_convolution_leaky_relu_3[grid(14400)](buf9,
            primals_7, buf2, buf10, buf11, 14400, XBLOCK=256, num_warps=4,
            num_stages=1)
        buf12 = extern_kernels.convolution(buf11, primals_8, 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, 30, 30), (3600, 900, 30, 1))
        buf13 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1),
            torch.bool)
        buf14 = empty_strided_cuda((4, 4, 30, 30), (3600, 900, 30, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_2[grid(14400)](buf12,
            primals_9, buf13, buf14, 14400, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf12
        del primals_9
        buf15 = extern_kernels.convolution(buf14, primals_10, 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, 30, 30), (3600, 900, 30, 1))
        buf16 = buf15
        del buf15
        triton_poi_fused_add_convolution_4[grid(14400)](buf16, primals_11,
            buf9, primals_7, buf2, 14400, XBLOCK=256, num_warps=4, num_stages=1
            )
        del buf2
        del buf9
        del primals_11
        del primals_7
    return (buf16, primals_1, primals_2, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf4, buf5, buf7, buf8, buf10, buf11, buf13,
        buf14)


class ResBlock(torch.nn.Module):

    def __init__(self, num_channel):
        super(ResBlock, self).__init__()
        self.conv1 = torch.nn.Conv2d(num_channel, num_channel, kernel_size=
            3, stride=1, padding=1)
        self.conv2 = torch.nn.Conv2d(num_channel, num_channel, kernel_size=
            3, stride=1, padding=1)
        self.leaky_relu = torch.nn.LeakyReLU()

    def forward(self, x):
        out = x
        out = self.leaky_relu(out)
        out = self.conv1(out)
        out = self.leaky_relu(out)
        out = self.conv2(out)
        return out + x


class ConvBlockNew(torch.nn.Module):

    def __init__(self, in_channel, num_channel):
        super(ConvBlockNew, self).__init__()
        self.conv = torch.nn.Conv2d(in_channel, num_channel, kernel_size=3,
            stride=1)
        self.max = torch.nn.MaxPool2d(kernel_size=3, stride=2)
        self.res1 = ResBlock(num_channel=num_channel)
        self.res2 = ResBlock(num_channel=num_channel)
        self.leaky_relu = torch.nn.LeakyReLU()

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_4 = self.res1.conv1.weight
        primals_5 = self.res1.conv1.bias
        primals_6 = self.res1.conv2.weight
        primals_7 = self.res1.conv2.bias
        primals_8 = self.res2.conv1.weight
        primals_9 = self.res2.conv1.bias
        primals_10 = self.res2.conv2.weight
        primals_11 = self.res2.conv2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
