# 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_8/inductor_cache/5e/c5ehnpzqdc7mhphgkqo4i3oou7srfelncenyblormj6cgjc4e3qd.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_2 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mm, %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=[1024], 
    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 = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kc/ckchl2fcn4qbavyhisqqlkodz7rsf6vkggqctr7auljdjzt3cz3d.py
# Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_4 => gt_1, mul_1, where_1
# Graph fragment:
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_1, 0.2), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mm_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=[2048], 
    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 = 2048
    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_8/inductor_cache/ap/capputshhaddkhcukzijl7e7uru52ybch5indnrni4id5vb7nxut.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_6 => gt_2, mul_2, where_2
# Graph fragment:
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_2, 0.2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %mm_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=[4096], 
    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 = 4096
    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_8/inductor_cache/nu/cnudiqa7ldo7zwv7c46jtaywfhr2btxm4hvnpsmqofu6sidtmbm4.py
# Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.tanh, aten.tanh_backward]
# Source node to ATen node mapping:
#   input_8 => tanh
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%mm_3,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %tanh), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %mul_3), kwargs = {})
triton_poi_fused_tanh_tanh_backward_3 = async_compile.triton('triton_poi_fused_tanh_tanh_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=[4096], 
    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_tanh_tanh_backward_3', 'mutated_arg_names': ['in_out_ptr0'], '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_tanh_tanh_backward_3(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = tmp1 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp2
    tl.store(in_out_ptr0 + (x0), tmp1, xmask)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (256, 4), (4, 1))
    assert_size_stride(primals_3, (512, 256), (256, 1))
    assert_size_stride(primals_4, (1024, 512), (512, 1))
    assert_size_stride(primals_5, (784, 1024), (1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 256), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf0, buf1, buf2, 1024, grid=grid(1024), stream=stream0)
        del buf0
        buf3 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, reinterpret_tensor(primals_3, (256, 512), (1, 256), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf3, buf4, buf5, 2048, grid=grid(2048), stream=stream0)
        del buf3
        buf6 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.mm]
        extern_kernels.mm(buf5, reinterpret_tensor(primals_4, (512, 1024), (1, 512), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf6, buf7, buf8, 4096, grid=grid(4096), stream=stream0)
        del buf6
        buf9 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_7], Original ATen: [aten.mm]
        extern_kernels.mm(buf8, reinterpret_tensor(primals_5, (1024, 784), (1, 1024), 0), out=buf9)
        buf10 = buf9; del buf9  # reuse
        buf11 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.tanh, aten.tanh_backward]
        triton_poi_fused_tanh_tanh_backward_3.run(buf10, buf11, 3136, grid=grid(3136), stream=stream0)
    return (reinterpret_tensor(buf10, (4, 1, 28, 28), (784, 784, 28, 1), 0), primals_1, buf1, buf2, buf4, buf5, buf7, buf8, buf11, primals_5, primals_4, primals_3, )


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), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((512, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1024, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((784, 1024), (1024, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 functools
import torch
import torch.utils.data
import torch
import torch.nn as nn


class DCGANGenerator_mnist(nn.Module):

    def __init__(self, z_dim, ngf=64, output_nc=1, norm_layer=nn.BatchNorm2d):
        super(DCGANGenerator_mnist, self).__init__()
        self.z_dim = z_dim
        self.ngf = ngf
        self.img_size = 28 * 28 * output_nc
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func != nn.BatchNorm2d
        else:
            use_bias = norm_layer != nn.BatchNorm2d
        self.fc1 = nn.Linear(self.z_dim, 256, bias=use_bias)
        self.fc2 = nn.Linear(self.fc1.out_features, self.fc1.out_features *
            2, bias=use_bias)
        self.fc3 = nn.Linear(self.fc2.out_features, self.fc2.out_features *
            2, bias=use_bias)
        self.fc4 = nn.Linear(self.fc3.out_features, self.img_size, bias=
            use_bias)
        self.model = nn.Sequential(self.fc1, nn.LeakyReLU(negative_slope=
            0.2), self.fc2, nn.LeakyReLU(negative_slope=0.2), self.fc3, nn.
            LeakyReLU(negative_slope=0.2), self.fc4, nn.Tanh())

    def forward(self, noise):
        img = self.model(noise.view(noise.shape[0], -1))
        return img.view(noise.shape[0], 1, 28, 28)


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


def get_init_inputs():
    return [[], {'z_dim': 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.triton_helpers import libdevice
import functools
import torch.utils.data
import torch
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@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_tanh_tanh_backward_3(in_out_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 3136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = tmp1 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp2
    tl.store(in_out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (256, 4), (4, 1))
    assert_size_stride(primals_3, (512, 256), (256, 1))
    assert_size_stride(primals_4, (1024, 512), (512, 1))
    assert_size_stride(primals_5, (784, 1024), (1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 256),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(1024)](buf0, buf1, buf2, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        buf3 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_3, (256, 512), (
            1, 256), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(2048)](buf3, buf4, buf5, 2048,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        buf6 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_4, (512, 1024),
            (1, 512), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        triton_poi_fused_leaky_relu_2[grid(4096)](buf6, buf7, buf8, 4096,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf6
        buf9 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_5, (1024, 784),
            (1, 1024), 0), out=buf9)
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        triton_poi_fused_tanh_tanh_backward_3[grid(3136)](buf10, buf11, 
            3136, XBLOCK=256, num_warps=4, num_stages=1)
    return (reinterpret_tensor(buf10, (4, 1, 28, 28), (784, 784, 28, 1), 0),
        primals_1, buf1, buf2, buf4, buf5, buf7, buf8, buf11, primals_5,
        primals_4, primals_3)


class DCGANGenerator_mnistNew(nn.Module):

    def __init__(self, z_dim, ngf=64, output_nc=1, norm_layer=nn.BatchNorm2d):
        super(DCGANGenerator_mnistNew, self).__init__()
        self.z_dim = z_dim
        self.ngf = ngf
        self.img_size = 28 * 28 * output_nc
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func != nn.BatchNorm2d
        else:
            use_bias = norm_layer != nn.BatchNorm2d
        self.fc1 = nn.Linear(self.z_dim, 256, bias=use_bias)
        self.fc2 = nn.Linear(self.fc1.out_features, self.fc1.out_features *
            2, bias=use_bias)
        self.fc3 = nn.Linear(self.fc2.out_features, self.fc2.out_features *
            2, bias=use_bias)
        self.fc4 = nn.Linear(self.fc3.out_features, self.img_size, bias=
            use_bias)
        self.model = nn.Sequential(self.fc1, nn.LeakyReLU(negative_slope=
            0.2), self.fc2, nn.LeakyReLU(negative_slope=0.2), self.fc3, nn.
            LeakyReLU(negative_slope=0.2), self.fc4, nn.Tanh())

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc2.weight
        primals_4 = self.fc3.weight
        primals_5 = self.fc4.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
