# 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/vz/cvzztlbfukyxov4xl4yqfdx5qim4yoxkstr3nyoywbvuzgqwj4sz.py
# Topologically Sorted Source Nodes: [conv1], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   conv1 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=2] = 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 = (%convolution, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, None)
    tl.store(out_ptr1 + (x0), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6b/c6ba6drsd2dfwhicxudugdemuumhw7ashkgyyqdim4x2wnkuqo7f.py
# Topologically Sorted Source Nodes: [conv2], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2 => gt_1, mul_1, where_1
# Graph fragment:
#   %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, 0.2), 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_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, None)
    tl.store(out_ptr1 + (x0), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vy/cvyauilkg7i7dscdqdox5qq672lwbmczsyf5tm3wmotudv5mckci.py
# Topologically Sorted Source Nodes: [conv3], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   conv3 => gt_2, mul_2, where_2
# Graph fragment:
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 0.2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
triton_poi_fused_leaky_relu_2 = async_compile.triton('triton_poi_fused_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, None)
    tl.store(out_ptr1 + (x0), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vw/cvwrfxuipgm22pi5ziuux4spez5kgdua7tqma6aouffvimvawq3v.py
# Topologically Sorted Source Nodes: [conv4], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   conv4 => gt_3, mul_3, where_3
# Graph fragment:
#   %gt_3 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 0.2), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_3), kwargs = {})
triton_poi_fused_leaky_relu_3 = async_compile.triton('triton_poi_fused_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, None)
    tl.store(out_ptr1 + (x0), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ry/crykxnwduk6jjr7hlj6qdo6vhbogxq66hvnfpazlzhjxppg3vj7r.py
# Topologically Sorted Source Nodes: [conv5], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   conv5 => gt_4, mul_4, where_4
# Graph fragment:
#   %gt_4 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_4, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_4, 0.2), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %convolution_4, %mul_4), kwargs = {})
triton_poi_fused_leaky_relu_4 = async_compile.triton('triton_poi_fused_leaky_relu_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_4(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, None)
    tl.store(out_ptr1 + (x0), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2w/c2wucpu6a3fsm74kupcidany43okvga25nesf5qww6mmtm2zsqtj.py
# Topologically Sorted Source Nodes: [conv6], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   conv6 => gt_5, mul_5, where_5
# Graph fragment:
#   %gt_5 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_5, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_5, 0.2), kwargs = {})
#   %where_5 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %convolution_5, %mul_5), kwargs = {})
triton_poi_fused_leaky_relu_5 = async_compile.triton('triton_poi_fused_leaky_relu_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, None)
    tl.store(out_ptr1 + (x0), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cp/ccpej7rouepiqf2zu7sric3dgxljz62fzjhnfhlo26eqtidutz62.py
# Topologically Sorted Source Nodes: [pool7, sigmoid], Original ATen: [aten.max_pool2d_with_indices, aten.sigmoid]
# Source node to ATen node mapping:
#   pool7 => _low_memory_max_pool2d_with_offsets, getitem_1
#   sigmoid => sigmoid
# 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_6, [2, 2], [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 = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_sigmoid_6 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_sigmoid_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_sigmoid_6', '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_max_pool2d_with_indices_sigmoid_6(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.sigmoid(tmp16)
    tl.store(out_ptr0 + (x0), tmp15, xmask)
    tl.store(out_ptr1 + (x0), tmp17, 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 = args
    args.clear()
    assert_size_stride(primals_1, (16, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (4, 2, 256, 256), (131072, 65536, 256, 1))
    assert_size_stride(primals_3, (32, 16, 4, 4), (256, 16, 4, 1))
    assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_6, (128, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_7, (128, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_8, (2, 128, 3, 3), (1152, 9, 3, 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_2, primals_1, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 128, 128), (262144, 16384, 128, 1))
        buf1 = empty_strided_cuda((4, 16, 128, 128), (262144, 16384, 128, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 16, 128, 128), (262144, 16384, 128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf0, buf1, buf2, 1048576, grid=grid(1048576), stream=stream0)
        del buf0
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf4 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf3, buf4, buf5, 524288, grid=grid(524288), stream=stream0)
        del buf3
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf7 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv3], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf6, buf7, buf8, 262144, grid=grid(262144), stream=stream0)
        del buf6
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_5, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf10 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv4], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_3.run(buf9, buf10, buf11, 131072, grid=grid(131072), stream=stream0)
        del buf9
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf13 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool)
        buf14 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv5], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_4.run(buf12, buf13, buf14, 32768, grid=grid(32768), stream=stream0)
        del buf12
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, primals_7, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf16 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.bool)
        buf17 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv6], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_5.run(buf15, buf16, buf17, 8192, grid=grid(8192), stream=stream0)
        del buf15
        # Topologically Sorted Source Nodes: [conv7], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 2, 2, 2), (8, 4, 2, 1))
        buf19 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.int8)
        buf20 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pool7, sigmoid], Original ATen: [aten.max_pool2d_with_indices, aten.sigmoid]
        triton_poi_fused_max_pool2d_with_indices_sigmoid_6.run(buf18, buf19, buf20, 8, grid=grid(8), stream=stream0)
    return (buf20, primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, buf1, buf2, buf4, buf5, buf7, buf8, buf10, buf11, buf13, buf14, buf16, buf17, buf18, buf19, buf20, )


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((16, 2, 4, 4), (32, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 2, 256, 256), (131072, 65536, 256, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, 16, 4, 4), (256, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 32, 4, 4), (512, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((2, 128, 3, 3), (1152, 9, 3, 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])
    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 Discriminator(nn.Module):

    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(2, 16, 4, 2, 1, bias=False)
        self.act1 = nn.LeakyReLU(0.2, inplace=False)
        self.conv2 = nn.Conv2d(16, 32, 4, 2, 1, bias=False)
        self.act2 = nn.LeakyReLU(0.2, inplace=False)
        self.conv3 = nn.Conv2d(32, 64, 4, 2, 1, bias=False)
        self.act3 = nn.LeakyReLU(0.2, inplace=False)
        self.conv4 = nn.Conv2d(64, 128, 4, 2, 1, bias=False)
        self.act4 = nn.LeakyReLU(0.2, inplace=False)
        self.conv5 = nn.Conv2d(128, 128, 4, 2, 1, bias=False)
        self.act5 = nn.LeakyReLU(0.2, inplace=False)
        self.conv6 = nn.Conv2d(128, 128, 4, 2, 1, bias=False)
        self.act6 = nn.LeakyReLU(0.2, inplace=False)
        self.conv7 = nn.Conv2d(128, 2, 3, 1, bias=False)
        self.pool7 = nn.MaxPool2d(2, stride=2)

    def forward(self, labels):
        conv1 = self.act1(self.conv1(labels))
        conv2 = self.act2(self.conv2(conv1))
        conv3 = self.act3(self.conv3(conv2))
        conv4 = self.act4(self.conv4(conv3))
        conv5 = self.act5(self.conv5(conv4))
        conv6 = self.act6(self.conv6(conv5))
        conv7 = self.conv7(conv6)
        pool7 = self.pool7(conv7)
        return torch.sigmoid(pool7)


def get_inputs():
    return [torch.rand([4, 2, 256, 256])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_4(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_5(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_sigmoid_6(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.sigmoid(tmp16)
    tl.store(out_ptr0 + x0, tmp15, xmask)
    tl.store(out_ptr1 + x0, tmp17, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (16, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (4, 2, 256, 256), (131072, 65536, 256, 1))
    assert_size_stride(primals_3, (32, 16, 4, 4), (256, 16, 4, 1))
    assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_6, (128, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_7, (128, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_8, (2, 128, 3, 3), (1152, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 128, 128), (262144, 16384, 128, 1))
        buf1 = empty_strided_cuda((4, 16, 128, 128), (262144, 16384, 128, 1
            ), torch.bool)
        buf2 = empty_strided_cuda((4, 16, 128, 128), (262144, 16384, 128, 1
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(1048576)](buf0, buf1, buf2, 
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf0
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf4 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf5 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_1[grid(524288)](buf3, buf4, buf5, 
            524288, XBLOCK=512, num_warps=8, num_stages=1)
        del buf3
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf7 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf8 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_2[grid(262144)](buf6, buf7, buf8, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf6
        buf9 = extern_kernels.convolution(buf8, primals_5, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf10 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf11 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_3[grid(131072)](buf9, buf10, buf11, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf9
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf13 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool
            )
        buf14 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_4[grid(32768)](buf12, buf13, buf14, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del buf12
        buf15 = extern_kernels.convolution(buf14, primals_7, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf16 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.bool
            )
        buf17 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_5[grid(8192)](buf15, buf16, buf17, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf15
        buf18 = extern_kernels.convolution(buf17, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 2, 2, 2), (8, 4, 2, 1))
        buf19 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.int8)
        buf20 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_sigmoid_6[grid(8)](buf18,
            buf19, buf20, 8, XBLOCK=8, num_warps=1, num_stages=1)
    return (buf20, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_6, primals_7, primals_8, buf1, buf2, buf4, buf5, buf7, buf8,
        buf10, buf11, buf13, buf14, buf16, buf17, buf18, buf19, buf20)


class DiscriminatorNew(nn.Module):

    def __init__(self):
        super(DiscriminatorNew, self).__init__()
        self.conv1 = nn.Conv2d(2, 16, 4, 2, 1, bias=False)
        self.act1 = nn.LeakyReLU(0.2, inplace=False)
        self.conv2 = nn.Conv2d(16, 32, 4, 2, 1, bias=False)
        self.act2 = nn.LeakyReLU(0.2, inplace=False)
        self.conv3 = nn.Conv2d(32, 64, 4, 2, 1, bias=False)
        self.act3 = nn.LeakyReLU(0.2, inplace=False)
        self.conv4 = nn.Conv2d(64, 128, 4, 2, 1, bias=False)
        self.act4 = nn.LeakyReLU(0.2, inplace=False)
        self.conv5 = nn.Conv2d(128, 128, 4, 2, 1, bias=False)
        self.act5 = nn.LeakyReLU(0.2, inplace=False)
        self.conv6 = nn.Conv2d(128, 128, 4, 2, 1, bias=False)
        self.act6 = nn.LeakyReLU(0.2, inplace=False)
        self.conv7 = nn.Conv2d(128, 2, 3, 1, bias=False)
        self.pool7 = nn.MaxPool2d(2, stride=2)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_4 = self.conv3.weight
        primals_5 = self.conv4.weight
        primals_6 = self.conv5.weight
        primals_7 = self.conv6.weight
        primals_8 = self.conv7.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
