# 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/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_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_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kn/ckn356huf5asavhbabfmbycymq6a24rohkhehai5tpvvcx6mz7ac.py
# Topologically Sorted Source Nodes: [advantage_mean, add, q], Original ATen: [aten.mean, aten.add, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   advantage_mean => mean
#   q => sub
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_9, [-1], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_11, %view_9), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mean), kwargs = {})
triton_poi_fused_add_mean_sub_1 = async_compile.triton('triton_poi_fused_add_mean_sub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_add_mean_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_mean_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (x2), xmask)
    tmp6 = tl.load(in_ptr2 + (4*x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp12 = tmp10 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp5 - tmp14
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    assert_size_stride(primals_12, (1, 4), (4, 1))
    assert_size_stride(primals_13, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 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, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf14, 256, grid=grid(256), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2  # reuse
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf13, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4  # reuse
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [adv_x], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf6, primals_7, buf12, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [advantage], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf7)
        del primals_11
        buf8 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [val_x], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf8, primals_9, buf11, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf9 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 4), (4, 1), 0), reinterpret_tensor(primals_12, (4, 1), (1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [advantage_mean, add, q], Original ATen: [aten.mean, aten.add, aten.sub]
        triton_poi_fused_add_mean_sub_1.run(buf9, primals_13, buf7, buf10, 256, grid=grid(256), stream=stream0)
        del buf7
        del buf9
        del primals_13
    return (buf10, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf6, (64, 4), (4, 1), 0), reinterpret_tensor(buf8, (64, 4), (4, 1), 0), primals_12, primals_10, buf11, primals_8, buf12, primals_6, buf13, primals_4, 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), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 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), (4, 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), (4, 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), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable


def identity(x: 'torch.Tensor') ->torch.Tensor:
    """Return input without any change."""
    return x


def init_layer_uniform(layer: 'nn.Linear', init_w: 'float'=0.003) ->nn.Linear:
    """Init uniform parameters on the single layer"""
    layer.weight.data.uniform_(-init_w, init_w)
    layer.bias.data.uniform_(-init_w, init_w)
    return layer


class MLP(nn.Module):
    """Baseline of Multilayer perceptron.

    Attributes:
        input_size (int): size of input
        output_size (int): size of output layer
        hidden_sizes (list): sizes of hidden layers
        hidden_activation (function): activation function of hidden layers
        output_activation (function): activation function of output layer
        hidden_layers (list): list containing linear layers
        use_output_layer (bool): whether or not to use the last layer
        n_category (int): category number (-1 if the action is continuous)

    """

    def __init__(self, input_size: 'int', output_size: 'int', hidden_sizes:
        'list', hidden_activation: 'Callable'=F.relu, output_activation:
        'Callable'=identity, linear_layer: 'nn.Module'=nn.Linear,
        use_output_layer: 'bool'=True, n_category: 'int'=-1, init_fn:
        'Callable'=init_layer_uniform):
        """Initialize.

        Args:
            input_size (int): size of input
            output_size (int): size of output layer
            hidden_sizes (list): number of hidden layers
            hidden_activation (function): activation function of hidden layers
            output_activation (function): activation function of output layer
            linear_layer (nn.Module): linear layer of mlp
            use_output_layer (bool): whether or not to use the last layer
            n_category (int): category number (-1 if the action is continuous)
            init_fn (Callable): weight initialization function bound for the last layer

        """
        super(MLP, self).__init__()
        self.hidden_sizes = hidden_sizes
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_activation = hidden_activation
        self.output_activation = output_activation
        self.linear_layer = linear_layer
        self.use_output_layer = use_output_layer
        self.n_category = n_category
        self.hidden_layers: 'list' = []
        in_size = self.input_size
        for i, next_size in enumerate(hidden_sizes):
            fc = self.linear_layer(in_size, next_size)
            in_size = next_size
            self.__setattr__('hidden_fc{}'.format(i), fc)
            self.hidden_layers.append(fc)
        if self.use_output_layer:
            self.output_layer = self.linear_layer(in_size, output_size)
            self.output_layer = init_fn(self.output_layer)
        else:
            self.output_layer = identity
            self.output_activation = identity

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        """Forward method implementation."""
        for hidden_layer in self.hidden_layers:
            x = self.hidden_activation(hidden_layer(x))
        x = self.output_activation(self.output_layer(x))
        return x


class NoisyMLPHandler:
    """Includes methods to handle noisy linear."""

    def reset_noise(self):
        """Re-sample noise"""
        for _, module in self.named_children():
            module.reset_noise()


class DuelingMLP(MLP, NoisyMLPHandler):
    """Multilayer perceptron with dueling construction."""

    def __init__(self, input_size: 'int', output_size: 'int', hidden_sizes:
        'list', hidden_activation: 'Callable'=F.relu, linear_layer:
        'nn.Module'=nn.Linear, init_fn: 'Callable'=init_layer_uniform):
        """Initialize."""
        super(DuelingMLP, self).__init__(input_size=input_size, output_size
            =output_size, hidden_sizes=hidden_sizes, hidden_activation=
            hidden_activation, linear_layer=linear_layer, use_output_layer=
            False)
        in_size = hidden_sizes[-1]
        self.advantage_hidden_layer = self.linear_layer(in_size, in_size)
        self.advantage_layer = self.linear_layer(in_size, output_size)
        self.advantage_layer = init_fn(self.advantage_layer)
        self.value_hidden_layer = self.linear_layer(in_size, in_size)
        self.value_layer = self.linear_layer(in_size, 1)
        self.value_layer = init_fn(self.value_layer)

    def _forward_dueling(self, x: 'torch.Tensor') ->torch.Tensor:
        adv_x = self.hidden_activation(self.advantage_hidden_layer(x))
        val_x = self.hidden_activation(self.value_hidden_layer(x))
        advantage = self.advantage_layer(adv_x)
        value = self.value_layer(val_x)
        advantage_mean = advantage.mean(dim=-1, keepdim=True)
        q = value + advantage - advantage_mean
        return q

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        """Forward method implementation."""
        x = super(DuelingMLP, self).forward(x)
        x = self._forward_dueling(x)
        return x


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


def get_init_inputs():
    return [[], {'input_size': 4, 'output_size': 4, 'hidden_sizes': [4, 4]}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable
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):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_mean_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + x2, xmask)
    tmp6 = tl.load(in_ptr2 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp12 = tmp10 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp5 - tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (1, 4), (4, 1))
    assert_size_stride(primals_13, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf14, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3,
            primals_5, buf13, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf6,
            primals_7, buf12, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf6, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf7)
        del primals_11
        buf8 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf8,
            primals_9, buf11, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf9 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_12, (4, 1), (1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mean_sub_1[grid(256)](buf9, primals_13, buf7,
            buf10, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf7
        del buf9
        del primals_13
    return (buf10, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
        buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf6, (64, 4), (4, 1),
        0), reinterpret_tensor(buf8, (64, 4), (4, 1), 0), primals_12,
        primals_10, buf11, primals_8, buf12, primals_6, buf13, primals_4, buf14
        )


def identity(x: 'torch.Tensor') ->torch.Tensor:
    """Return input without any change."""
    return x


def init_layer_uniform(layer: 'nn.Linear', init_w: 'float'=0.003) ->nn.Linear:
    """Init uniform parameters on the single layer"""
    layer.weight.data.uniform_(-init_w, init_w)
    layer.bias.data.uniform_(-init_w, init_w)
    return layer


class MLP(nn.Module):
    """Baseline of Multilayer perceptron.

    Attributes:
        input_size (int): size of input
        output_size (int): size of output layer
        hidden_sizes (list): sizes of hidden layers
        hidden_activation (function): activation function of hidden layers
        output_activation (function): activation function of output layer
        hidden_layers (list): list containing linear layers
        use_output_layer (bool): whether or not to use the last layer
        n_category (int): category number (-1 if the action is continuous)

    """

    def __init__(self, input_size: 'int', output_size: 'int', hidden_sizes:
        'list', hidden_activation: 'Callable'=F.relu, output_activation:
        'Callable'=identity, linear_layer: 'nn.Module'=nn.Linear,
        use_output_layer: 'bool'=True, n_category: 'int'=-1, init_fn:
        'Callable'=init_layer_uniform):
        """Initialize.

        Args:
            input_size (int): size of input
            output_size (int): size of output layer
            hidden_sizes (list): number of hidden layers
            hidden_activation (function): activation function of hidden layers
            output_activation (function): activation function of output layer
            linear_layer (nn.Module): linear layer of mlp
            use_output_layer (bool): whether or not to use the last layer
            n_category (int): category number (-1 if the action is continuous)
            init_fn (Callable): weight initialization function bound for the last layer

        """
        super(MLP, self).__init__()
        self.hidden_sizes = hidden_sizes
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_activation = hidden_activation
        self.output_activation = output_activation
        self.linear_layer = linear_layer
        self.use_output_layer = use_output_layer
        self.n_category = n_category
        self.hidden_layers: 'list' = []
        in_size = self.input_size
        for i, next_size in enumerate(hidden_sizes):
            fc = self.linear_layer(in_size, next_size)
            in_size = next_size
            self.__setattr__('hidden_fc{}'.format(i), fc)
            self.hidden_layers.append(fc)
        if self.use_output_layer:
            self.output_layer = self.linear_layer(in_size, output_size)
            self.output_layer = init_fn(self.output_layer)
        else:
            self.output_layer = identity
            self.output_activation = identity

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        """Forward method implementation."""
        for hidden_layer in self.hidden_layers:
            x = self.hidden_activation(hidden_layer(x))
        x = self.output_activation(self.output_layer(x))
        return x


class NoisyMLPHandler:
    """Includes methods to handle noisy linear."""

    def reset_noise(self):
        """Re-sample noise"""
        for _, module in self.named_children():
            module.reset_noise()


class DuelingMLPNew(MLP, NoisyMLPHandler):
    """Multilayer perceptron with dueling construction."""

    def __init__(self, input_size: 'int', output_size: 'int', hidden_sizes:
        'list', hidden_activation: 'Callable'=F.relu, linear_layer:
        'nn.Module'=nn.Linear, init_fn: 'Callable'=init_layer_uniform):
        """Initialize."""
        super(DuelingMLPNew, self).__init__(input_size=input_size,
            output_size=output_size, hidden_sizes=hidden_sizes,
            hidden_activation=hidden_activation, linear_layer=linear_layer,
            use_output_layer=False)
        in_size = hidden_sizes[-1]
        self.advantage_hidden_layer = self.linear_layer(in_size, in_size)
        self.advantage_layer = self.linear_layer(in_size, output_size)
        self.advantage_layer = init_fn(self.advantage_layer)
        self.value_hidden_layer = self.linear_layer(in_size, in_size)
        self.value_layer = self.linear_layer(in_size, 1)
        self.value_layer = init_fn(self.value_layer)

    def _forward_dueling(self, x: 'torch.Tensor') ->torch.Tensor:
        adv_x = self.hidden_activation(self.advantage_hidden_layer(x))
        val_x = self.hidden_activation(self.value_hidden_layer(x))
        advantage = self.advantage_layer(adv_x)
        value = self.value_layer(val_x)
        advantage_mean = advantage.mean(dim=-1, keepdim=True)
        q = value + advantage - advantage_mean
        return q

    def forward(self, input_0):
        primals_1 = self.hidden_fc0.weight
        primals_2 = self.hidden_fc0.bias
        primals_4 = self.hidden_fc1.weight
        primals_5 = self.hidden_fc1.bias
        primals_6 = self.advantage_hidden_layer.weight
        primals_7 = self.advantage_hidden_layer.bias
        primals_8 = self.advantage_layer.weight
        primals_9 = self.advantage_layer.bias
        primals_10 = self.value_hidden_layer.weight
        primals_11 = self.value_hidden_layer.bias
        primals_12 = self.value_layer.weight
        primals_13 = self.value_layer.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
