# 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/cb/ccbbqnqwpjfpv7rbn5c6tvco7t66xtsppx76wxpogrg2vmzxziy2.py
# Topologically Sorted Source Nodes: [add, relu6, truediv, x], Original ATen: [aten.add, aten.hardtanh, aten.div, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   relu6 => clamp_max, clamp_min
#   truediv => div
#   x => mul
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, 3), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 6), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, 6), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %view_1), kwargs = {})
triton_poi_fused_add_div_hardtanh_mul_0 = async_compile.triton('triton_poi_fused_add_div_hardtanh_mul_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=[8192], 
    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_add_div_hardtanh_mul_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_add_div_hardtanh_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6144
    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 = 3.0
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = 0.16666666666666666
    tmp8 = tmp6 * tmp7
    tmp9 = tmp8 * tmp0
    tl.store(out_ptr0 + (x0), tmp9, 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, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14 = args
    args.clear()
    assert_size_stride(primals_1, (96, 4), (4, 1))
    assert_size_stride(primals_2, (96, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (96, 96), (96, 1))
    assert_size_stride(primals_5, (96, ), (1, ))
    assert_size_stride(primals_6, (4, 96), (96, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (1, 4), (4, 1))
    assert_size_stride(primals_9, (96, 4), (4, 1))
    assert_size_stride(primals_10, (96, ), (1, ))
    assert_size_stride(primals_11, (96, 96), (96, 1))
    assert_size_stride(primals_12, (96, ), (1, ))
    assert_size_stride(primals_13, (1, 96), (96, 1))
    assert_size_stride(primals_14, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 96), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, relu6, truediv, x], Original ATen: [aten.add, aten.hardtanh, aten.div, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_hardtanh_mul_0.run(buf0, buf1, 6144, grid=grid(6144), stream=stream0)
        buf2 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 96), (96, 1), 0), reinterpret_tensor(primals_4, (96, 96), (1, 96), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_1, relu6_1, truediv_1, x_1], Original ATen: [aten.add, aten.hardtanh, aten.div, aten.mul]
        triton_poi_fused_add_div_hardtanh_mul_0.run(buf2, buf3, 6144, grid=grid(6144), stream=stream0)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [action_mean], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 96), (96, 1), 0), reinterpret_tensor(primals_6, (96, 4), (1, 96), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_10, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 96), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_10
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_2, relu6_2, truediv_2, x_2], Original ATen: [aten.add, aten.hardtanh, aten.div, aten.mul]
        triton_poi_fused_add_div_hardtanh_mul_0.run(buf5, buf6, 6144, grid=grid(6144), stream=stream0)
        buf7 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf6, (64, 96), (96, 1), 0), reinterpret_tensor(primals_11, (96, 96), (1, 96), 0), alpha=1, beta=1, out=buf7)
        del primals_12
        buf8 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_3, relu6_3, truediv_3, x_3], Original ATen: [aten.add, aten.hardtanh, aten.div, aten.mul]
        triton_poi_fused_add_div_hardtanh_mul_0.run(buf7, buf8, 6144, grid=grid(6144), stream=stream0)
        buf10 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [critic_value], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_14, reinterpret_tensor(buf8, (64, 96), (96, 1), 0), reinterpret_tensor(primals_13, (96, 1), (1, 96), 0), alpha=1, beta=1, out=buf10)
        del primals_14
    return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_8, (4, 4, 4, 4), (0, 0, 0, 1), 0), reinterpret_tensor(buf10, (4, 4, 4, 1), (16, 4, 1, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (64, 96), (96, 1), 0), buf2, reinterpret_tensor(buf3, (64, 96), (96, 1), 0), buf5, reinterpret_tensor(buf6, (64, 96), (96, 1), 0), buf7, reinterpret_tensor(buf8, (64, 96), (96, 1), 0), primals_13, primals_11, primals_6, primals_4, )


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((96, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((96, ), (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((96, 96), (96, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 96), (96, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((96, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((96, 96), (96, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((1, 96), (96, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = 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, primals_14])
    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 numpy as np
import torch.nn as nn
import torch.nn.functional as F


def f_hard_swish(x):
    return F.relu6(x + 3) / 6 * x


class ActorCritic(nn.Module):

    def __init__(self, num_inputs, num_outputs, layer_norm=True):
        super(ActorCritic, self).__init__()
        mid_dim = 96
        self.actor_fc1 = nn.Linear(num_inputs, mid_dim)
        self.actor_fc2 = nn.Linear(mid_dim, mid_dim)
        self.actor_fc3 = nn.Linear(mid_dim, num_outputs)
        self.actor_logstd = nn.Parameter(torch.zeros(1, num_outputs))
        self.critic_fc1 = nn.Linear(num_inputs, mid_dim)
        self.critic_fc2 = nn.Linear(mid_dim, mid_dim)
        self.critic_fc3 = nn.Linear(mid_dim, 1)
        if layer_norm:
            self.layer_norm(self.actor_fc1, std=1.0)
            self.layer_norm(self.actor_fc2, std=1.0)
            self.layer_norm(self.actor_fc3, std=0.01)
            self.layer_norm(self.critic_fc1, std=1.0)
            self.layer_norm(self.critic_fc2, std=1.0)
            self.layer_norm(self.critic_fc3, std=1.0)

    @staticmethod
    def layer_norm(layer, std=1.0, bias_const=0.0):
        torch.nn.init.orthogonal_(layer.weight, std)
        torch.nn.init.constant_(layer.bias, bias_const)

    def forward(self, states):
        """
        run policy network (actor) as well as value network (critic)
        :param states: a Tensor2 represents states
        :return: 3 Tensor2
        """
        action_mean, action_logstd = self._forward_actor(states)
        critic_value = self._forward_critic(states)
        return action_mean, action_logstd, critic_value

    def _forward_actor(self, states):
        x = f_hard_swish(self.actor_fc1(states))
        x = f_hard_swish(self.actor_fc2(x))
        action_mean = self.actor_fc3(x)
        action_logstd = self.actor_logstd.expand_as(action_mean)
        return action_mean, action_logstd

    def _forward_critic(self, states):
        x = f_hard_swish(self.critic_fc1(states))
        x = f_hard_swish(self.critic_fc2(x))
        critic_value = self.critic_fc3(x)
        return critic_value

    def select_action(self, action_mean, action_logstd, return_logproba=True):
        """
        given mean and std, sample an action from normal(mean, std)
        also returns probability of the given chosen
        """
        action_std = torch.exp(action_logstd)
        action = torch.normal(action_mean, action_std)
        if return_logproba:
            logproba = self._normal_logproba(action, action_mean,
                action_logstd, action_std)
        return action, logproba

    @staticmethod
    def _normal_logproba(x, mean, logstd, std=None):
        if std is None:
            std = torch.exp(logstd)
        std_sq = std.pow(2)
        logproba = -0.5 * np.log(2 * np.pi) - logstd - (x - mean).pow(2) / (
            2 * std_sq)
        return logproba.sum(1)

    def get_logproba(self, states, actions):
        """
        return probability of chosen the given actions under corresponding states of current network
        :param states: Tensor
        :param actions: Tensor
        """
        action_mean, action_logstd = self._forward_actor(states)
        logproba = self._normal_logproba(actions, action_mean, action_logstd)
        return logproba


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


def get_init_inputs():
    return [[], {'num_inputs': 4, 'num_outputs': 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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
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_add_div_hardtanh_mul_0(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
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 3.0
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = 0.16666666666666666
    tmp8 = tmp6 * tmp7
    tmp9 = tmp8 * tmp0
    tl.store(out_ptr0 + x0, tmp9, None)


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, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (96, 4), (4, 1))
    assert_size_stride(primals_2, (96,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (96, 96), (96, 1))
    assert_size_stride(primals_5, (96,), (1,))
    assert_size_stride(primals_6, (4, 96), (96, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 4), (4, 1))
    assert_size_stride(primals_9, (96, 4), (4, 1))
    assert_size_stride(primals_10, (96,), (1,))
    assert_size_stride(primals_11, (96, 96), (96, 1))
    assert_size_stride(primals_12, (96,), (1,))
    assert_size_stride(primals_13, (1, 96), (96, 1))
    assert_size_stride(primals_14, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 96), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_hardtanh_mul_0[grid(6144)](buf0, buf1, 
            6144, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 96),
            (96, 1), 0), reinterpret_tensor(primals_4, (96, 96), (1, 96), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.
            float32)
        triton_poi_fused_add_div_hardtanh_mul_0[grid(6144)](buf2, buf3, 
            6144, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 96),
            (96, 1), 0), reinterpret_tensor(primals_6, (96, 4), (1, 96), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(primals_3, (64,
            4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 96), (1, 4), 
            0), alpha=1, beta=1, out=buf5)
        del primals_10
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.
            float32)
        triton_poi_fused_add_div_hardtanh_mul_0[grid(6144)](buf5, buf6, 
            6144, XBLOCK=128, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((64, 96), (96, 1), torch.float32)
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf6, (64, 96),
            (96, 1), 0), reinterpret_tensor(primals_11, (96, 96), (1, 96), 
            0), alpha=1, beta=1, out=buf7)
        del primals_12
        buf8 = empty_strided_cuda((4, 4, 4, 96), (1536, 384, 96, 1), torch.
            float32)
        triton_poi_fused_add_div_hardtanh_mul_0[grid(6144)](buf7, buf8, 
            6144, XBLOCK=128, num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_14, reinterpret_tensor(buf8, (64, 96),
            (96, 1), 0), reinterpret_tensor(primals_13, (96, 1), (1, 96), 0
            ), alpha=1, beta=1, out=buf10)
        del primals_14
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_8, (4, 4, 4, 4), (0, 0, 0, 1), 0
        ), reinterpret_tensor(buf10, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf1, (64, 96), (96, 1), 0
        ), buf2, reinterpret_tensor(buf3, (64, 96), (96, 1), 0
        ), buf5, reinterpret_tensor(buf6, (64, 96), (96, 1), 0
        ), buf7, reinterpret_tensor(buf8, (64, 96), (96, 1), 0
        ), primals_13, primals_11, primals_6, primals_4


def f_hard_swish(x):
    return F.relu6(x + 3) / 6 * x


class ActorCriticNew(nn.Module):

    def __init__(self, num_inputs, num_outputs, layer_norm=True):
        super(ActorCriticNew, self).__init__()
        mid_dim = 96
        self.actor_fc1 = nn.Linear(num_inputs, mid_dim)
        self.actor_fc2 = nn.Linear(mid_dim, mid_dim)
        self.actor_fc3 = nn.Linear(mid_dim, num_outputs)
        self.actor_logstd = nn.Parameter(torch.zeros(1, num_outputs))
        self.critic_fc1 = nn.Linear(num_inputs, mid_dim)
        self.critic_fc2 = nn.Linear(mid_dim, mid_dim)
        self.critic_fc3 = nn.Linear(mid_dim, 1)
        if layer_norm:
            self.layer_norm(self.actor_fc1, std=1.0)
            self.layer_norm(self.actor_fc2, std=1.0)
            self.layer_norm(self.actor_fc3, std=0.01)
            self.layer_norm(self.critic_fc1, std=1.0)
            self.layer_norm(self.critic_fc2, std=1.0)
            self.layer_norm(self.critic_fc3, std=1.0)

    @staticmethod
    def layer_norm(layer, std=1.0, bias_const=0.0):
        torch.nn.init.orthogonal_(layer.weight, std)
        torch.nn.init.constant_(layer.bias, bias_const)

    def _forward_actor(self, states):
        x = f_hard_swish(self.actor_fc1(states))
        x = f_hard_swish(self.actor_fc2(x))
        action_mean = self.actor_fc3(x)
        action_logstd = self.actor_logstd.expand_as(action_mean)
        return action_mean, action_logstd

    def _forward_critic(self, states):
        x = f_hard_swish(self.critic_fc1(states))
        x = f_hard_swish(self.critic_fc2(x))
        critic_value = self.critic_fc3(x)
        return critic_value

    def select_action(self, action_mean, action_logstd, return_logproba=True):
        """
        given mean and std, sample an action from normal(mean, std)
        also returns probability of the given chosen
        """
        action_std = torch.exp(action_logstd)
        action = torch.normal(action_mean, action_std)
        if return_logproba:
            logproba = self._normal_logproba(action, action_mean,
                action_logstd, action_std)
        return action, logproba

    @staticmethod
    def _normal_logproba(x, mean, logstd, std=None):
        if std is None:
            std = torch.exp(logstd)
        std_sq = std.pow(2)
        logproba = -0.5 * np.log(2 * np.pi) - logstd - (x - mean).pow(2) / (
            2 * std_sq)
        return logproba.sum(1)

    def get_logproba(self, states, actions):
        """
        return probability of chosen the given actions under corresponding states of current network
        :param states: Tensor
        :param actions: Tensor
        """
        action_mean, action_logstd = self._forward_actor(states)
        logproba = self._normal_logproba(actions, action_mean, action_logstd)
        return logproba

    def forward(self, input_0):
        primals_8 = self.actor_logstd
        primals_1 = self.actor_fc1.weight
        primals_2 = self.actor_fc1.bias
        primals_4 = self.actor_fc2.weight
        primals_5 = self.actor_fc2.bias
        primals_6 = self.actor_fc3.weight
        primals_7 = self.actor_fc3.bias
        primals_9 = self.critic_fc1.weight
        primals_10 = self.critic_fc1.bias
        primals_11 = self.critic_fc2.weight
        primals_12 = self.critic_fc2.bias
        primals_13 = self.critic_fc3.weight
        primals_14 = self.critic_fc3.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, primals_14])
        return output[0], output[1], output[2]
