# 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_1/inductor_cache/ee/ceelnmyyuq5vt6keqkoyx42in3lgxsjzl3w5zrsxshnjzs235atg.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_1 => relu
# Graph fragment:
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%view_6, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, 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)
    x4 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x4), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x4), tmp4, None)
    tl.store(out_ptr0 + (x4), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/2b/c2boo4jotsghgwkvi3bhh7jeeqfoifmqf3i5xpefsjudgfwsofmd.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   x_2 => view_7
# Graph fragment:
#   %view_7 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_6, [64, 64]), kwargs = {})
triton_poi_fused_view_1 = async_compile.triton('triton_poi_fused_view_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_view_1(in_ptr0, out_ptr0, 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 % 64
    x1 = (xindex // 64)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1) + (256*((x1 % 4) // 4)) + (1024*(((4*((x1 // 4) % 4)) + (x1 % 4)) // 16))), None)
    tl.store(out_ptr0 + (x2), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/zn/czn2rtp4skukdswua2ptjqd2a7mxp4omcm4gbcgfjj4llz6orj2f.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu, aten.view, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_5 => relu_2, view_17
# Graph fragment:
#   %relu_2 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_15,), kwargs = {})
#   %view_17 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%view_16, [4, 4, 4, 64]), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%view_22, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_view_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_view_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: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_view_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_view_2(in_out_ptr0, 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)
    x4 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x4), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x4), tmp4, None)
    tl.store(out_ptr1 + (x4), tmp6, None)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (64, 4), (4, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 64), (64, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (64, 64), (64, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf11, 4096, grid=grid(4096), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf1, buf2, 4096, grid=grid(4096), stream=stream0)
        buf3 = reinterpret_tensor(buf1, (64, 64), (64, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf3  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf4, primals_5, buf10, 4096, grid=grid(4096), stream=stream0)
        del primals_5
        buf5 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf4, buf5, 4096, grid=grid(4096), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (64, 64), (64, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (64, 64), (1, 64), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf6  # reuse
        buf8 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu, aten.view, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_view_2.run(buf7, primals_7, buf8, buf9, 4096, grid=grid(4096), stream=stream0)
        del buf7
        del primals_7
    return (buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, buf5, buf9, primals_6, buf10, primals_4, buf11, )


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((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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.utils.data
import torch.nn as nn


def _get_activation(activation):
    valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
    assert activation in valid, 'activation should be one of {}'.format(valid)
    if activation == 'relu':
        return nn.ReLU(inplace=True)
    if activation == 'leaky_relu' or activation == 'lrelu':
        return nn.LeakyReLU(inplace=True)
    if activation == 'sigmoid':
        return nn.Sigmoid()
    if activation == 'tanh':
        return nn.Tanh()
    return None


def _init_fc_or_conv(fc_conv, activation):
    gain = 1.0
    if activation is not None:
        gain = nn.init.calculate_gain(activation)
    nn.init.xavier_uniform_(fc_conv.weight, gain)
    if fc_conv.bias is not None:
        nn.init.constant_(fc_conv.bias, 0.0)


class FCModule(nn.Module):
    """Basic fully connected module with optional dropout.

    Args:
      n_in(int): number of input channels.
      n_out(int): number of output channels.
      activation(str): nonlinear activation function.
      dropout(float): dropout ratio if defined, default to None: no dropout.
    """

    def __init__(self, n_in, n_out, activation=None, dropout=None):
        super(FCModule, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer'
        assert isinstance(n_out, int
            ) and n_out > 0, 'Output channels should be a positive integer'
        self.add_module('fc', nn.Linear(n_in, n_out))
        if activation is not None:
            self.add_module('activation', _get_activation(activation))
        if dropout is not None:
            self.add_module('dropout', nn.Dropout(dropout, inplace=True))
        _init_fc_or_conv(self.fc, activation)

    def forward(self, x):
        for c in self.children():
            x = c(x)
        return x


class FCChain(nn.Module):
    """Linear chain of fully connected layers.

    Args:
      n_in(int): number of input channels.
      width(int or list of int): number of features channels in the intermediate layers.
      depth(int): number of layers
      activation(str): nonlinear activation function between convolutions.
      dropout(float or list of float): dropout ratio if defined, default to None: no dropout.
    """

    def __init__(self, n_in, width=64, depth=3, activation='relu', dropout=None
        ):
        super(FCChain, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer'
        assert isinstance(depth, int
            ) and depth > 0, 'Depth should be a positive integer'
        assert isinstance(width, int) or isinstance(width, list
            ), 'Width should be a list or an int'
        _in = [n_in]
        if isinstance(width, int):
            _in = _in + [width] * (depth - 1)
            _out = [width] * depth
        elif isinstance(width, list):
            assert len(width
                ) == depth, 'Specifying width with a least: should have `depth` entries'
            _in = _in + width[:-1]
            _out = width
        _activations = [activation] * depth
        if dropout is not None:
            assert isinstance(dropout, float) or isinstance(dropout, list
                ), 'Dropout should be a float or a list of floats'
        if dropout is None or isinstance(dropout, float):
            _dropout = [dropout] * depth
        elif isinstance(dropout, list):
            assert len(dropout
                ) == depth, "When specifying a list of dropout, the list should have 'depth' elements."
            _dropout = dropout
        for lvl in range(depth):
            self.add_module('fc{}'.format(lvl), FCModule(_in[lvl], _out[lvl
                ], activation=_activations[lvl], dropout=_dropout[lvl]))

    def forward(self, x):
        for m in self.children():
            x = m(x)
        return x


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


def get_init_inputs():
    return [[], {'n_in': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x4 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x4, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, None)
    tl.store(out_ptr0 + x4, tmp6, None)


@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1 + 256 * (x1 % 4 // 4) + 1024 * (
        (4 * (x1 // 4 % 4) + x1 % 4) // 16)), None)
    tl.store(out_ptr0 + x2, tmp0, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_view_2(in_out_ptr0, 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)
    x4 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x4, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x4, tmp4, None)
    tl.store(out_ptr1 + x4, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (64, 4), (4, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 64), (64, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64), (64, 1))
    assert_size_stride(primals_7, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf0
        buf11 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf1,
            primals_2, buf11, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        triton_poi_fused_view_1[grid(4096)](buf1, buf2, 4096, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (64, 64), (64, 1), 0)
        del buf1
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (64, 64), (1,
            64), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf3
        buf10 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf4,
            primals_5, buf10, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        triton_poi_fused_view_1[grid(4096)](buf4, buf5, 4096, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (64, 64), (64, 1), 0)
        del buf4
        extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (64, 64), (1,
            64), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
            float32)
        buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
            )
        triton_poi_fused_relu_threshold_backward_view_2[grid(4096)](buf7,
            primals_7, buf8, buf9, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del buf7
        del primals_7
    return buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf2, buf5, buf9, primals_6, buf10, primals_4, buf11


def _get_activation(activation):
    valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
    assert activation in valid, 'activation should be one of {}'.format(valid)
    if activation == 'relu':
        return nn.ReLU(inplace=True)
    if activation == 'leaky_relu' or activation == 'lrelu':
        return nn.LeakyReLU(inplace=True)
    if activation == 'sigmoid':
        return nn.Sigmoid()
    if activation == 'tanh':
        return nn.Tanh()
    return None


def _init_fc_or_conv(fc_conv, activation):
    gain = 1.0
    if activation is not None:
        gain = nn.init.calculate_gain(activation)
    nn.init.xavier_uniform_(fc_conv.weight, gain)
    if fc_conv.bias is not None:
        nn.init.constant_(fc_conv.bias, 0.0)


class FCModule(nn.Module):
    """Basic fully connected module with optional dropout.

    Args:
      n_in(int): number of input channels.
      n_out(int): number of output channels.
      activation(str): nonlinear activation function.
      dropout(float): dropout ratio if defined, default to None: no dropout.
    """

    def __init__(self, n_in, n_out, activation=None, dropout=None):
        super(FCModule, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer'
        assert isinstance(n_out, int
            ) and n_out > 0, 'Output channels should be a positive integer'
        self.add_module('fc', nn.Linear(n_in, n_out))
        if activation is not None:
            self.add_module('activation', _get_activation(activation))
        if dropout is not None:
            self.add_module('dropout', nn.Dropout(dropout, inplace=True))
        _init_fc_or_conv(self.fc, activation)

    def forward(self, x):
        for c in self.children():
            x = c(x)
        return x


class FCChainNew(nn.Module):
    """Linear chain of fully connected layers.

    Args:
      n_in(int): number of input channels.
      width(int or list of int): number of features channels in the intermediate layers.
      depth(int): number of layers
      activation(str): nonlinear activation function between convolutions.
      dropout(float or list of float): dropout ratio if defined, default to None: no dropout.
    """

    def __init__(self, n_in, width=64, depth=3, activation='relu', dropout=None
        ):
        super(FCChainNew, self).__init__()
        assert isinstance(n_in, int
            ) and n_in > 0, 'Input channels should be a positive integer'
        assert isinstance(depth, int
            ) and depth > 0, 'Depth should be a positive integer'
        assert isinstance(width, int) or isinstance(width, list
            ), 'Width should be a list or an int'
        _in = [n_in]
        if isinstance(width, int):
            _in = _in + [width] * (depth - 1)
            _out = [width] * depth
        elif isinstance(width, list):
            assert len(width
                ) == depth, 'Specifying width with a least: should have `depth` entries'
            _in = _in + width[:-1]
            _out = width
        _activations = [activation] * depth
        if dropout is not None:
            assert isinstance(dropout, float) or isinstance(dropout, list
                ), 'Dropout should be a float or a list of floats'
        if dropout is None or isinstance(dropout, float):
            _dropout = [dropout] * depth
        elif isinstance(dropout, list):
            assert len(dropout
                ) == depth, "When specifying a list of dropout, the list should have 'depth' elements."
            _dropout = dropout
        for lvl in range(depth):
            self.add_module('fc{}'.format(lvl), FCModule(_in[lvl], _out[lvl
                ], activation=_activations[lvl], dropout=_dropout[lvl]))

    def forward(self, input_0):
        primals_1 = self.fc0.fc.weight
        primals_2 = self.fc0.fc.bias
        primals_4 = self.fc1.fc.weight
        primals_5 = self.fc1.fc.bias
        primals_6 = self.fc2.fc.weight
        primals_7 = self.fc2.fc.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
