# 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_7/inductor_cache/6o/c6o7ainbzocsswla76yvmdsc5donraaar3dzlx2icwrueb7fc46u.py
# Topologically Sorted Source Nodes: [a], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   a => relu
# Graph fragment:
#   %relu : [num_users=2] = 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 = (%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=[16384], 
    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 = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), 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 + (x2), tmp4, None)
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4b/c4b2hzwtyukbvevcudxoz7y4xx3uv32hdiaveepa7oirsasngwf5.py
# Topologically Sorted Source Nodes: [tanh, mean, softplus, sigma, mul_1, add_1, action], Original ATen: [aten.tanh, aten.mul, aten.softplus, aten.add, aten.clamp]
# Source node to ATen node mapping:
#   action => clamp_max, clamp_min
#   add_1 => add_1
#   mean => mul
#   mul_1 => mul_1
#   sigma => add
#   softplus => exp, gt, log1p, where
#   tanh => tanh
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_5,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, 4), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%view_7,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 20), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_7, %log1p), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where, 0.001), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%normal_functional, %add), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_1, -4), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 4), kwargs = {})
triton_poi_fused_add_clamp_mul_softplus_tanh_1 = async_compile.triton('triton_poi_fused_add_clamp_mul_softplus_tanh_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_clamp_mul_softplus_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_clamp_mul_softplus_tanh_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp4 = tl.load(in_ptr1 + (x0), xmask)
    tmp5 = tl.load(in_ptr2 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp6 = 20.0
    tmp7 = tmp5 > tmp6
    tmp8 = tl_math.exp(tmp5)
    tmp9 = libdevice.log1p(tmp8)
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tmp11 = 0.001
    tmp12 = tmp10 + tmp11
    tmp13 = tmp4 * tmp12
    tmp14 = tmp3 + tmp13
    tmp15 = -4.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = triton_helpers.minimum(tmp16, tmp2)
    tl.store(out_ptr0 + (x0), tmp17, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (256, 4), (4, 1))
    assert_size_stride(primals_2, (256, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (256, 256), (256, 1))
    assert_size_stride(primals_5, (256, ), (1, ))
    assert_size_stride(primals_6, (4, 256), (256, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 256), (256, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 256), (256, 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, 256), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 256), (4096, 1024, 256, 1), 0); del buf0  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.bool)
        # Topologically Sorted Source Nodes: [a], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf11, 16384, grid=grid(16384), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 256), (256, 1), 0), reinterpret_tensor(primals_4, (256, 256), (1, 256), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 256), (4096, 1024, 256, 1), 0); del buf2  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.bool)
        # Topologically Sorted Source Nodes: [a_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf10, 16384, grid=grid(16384), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 256), (256, 1), 0), reinterpret_tensor(primals_6, (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 256), (256, 1), 0), reinterpret_tensor(primals_8, (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.normal_functional]
        buf7 = torch.ops.aten.normal_functional.default(buf6)
        buf8 = buf7
        del buf7
        buf9 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [tanh, mean, softplus, sigma, mul_1, add_1, action], Original ATen: [aten.tanh, aten.mul, aten.softplus, aten.add, aten.clamp]
        triton_poi_fused_add_clamp_mul_softplus_tanh_1.run(buf4, buf8, buf5, buf9, 256, grid=grid(256), stream=stream0)
    return (buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 256), (256, 1), 0), reinterpret_tensor(buf3, (64, 256), (256, 1), 0), buf4, buf5, buf8, primals_8, 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((256, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, ), (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((256, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (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])
    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 torch.distributions import Normal


class Actor(nn.Module):

    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.l1 = nn.Linear(state_dim, 256)
        self.l2 = nn.Linear(256, 256)
        self.l3_sigma = nn.Linear(256, action_dim)
        self.l3_mean = nn.Linear(256, action_dim)
        self.max_action = max_action
        self.action_dim = action_dim
        self.state_dim = state_dim

    def forward(self, state, *args):
        a = F.relu(self.l1(state))
        a = F.relu(self.l2(a))
        mean = self.max_action * torch.tanh(self.l3_mean(a))
        sigma = F.softplus(self.l3_sigma(a)) + 0.001
        normal = Normal(mean, sigma)
        action = normal.rsample().clamp(-self.max_action, self.max_action)
        return action

    def forward_all(self, state, *args):
        a = F.relu(self.l1(state))
        a = F.relu(self.l2(a))
        mean = self.max_action * torch.tanh(self.l3_mean(a))
        sigma = F.softplus(self.l3_sigma(a)) + 0.001
        normal = Normal(mean, sigma)
        action = normal.rsample()
        log_prob = normal.log_prob(action).sum(1, keepdim=True)
        action = action.clamp(-self.max_action, self.max_action)
        return action, log_prob, mean, sigma

    def forward_all_sample(self, state, *args):
        a = F.relu(self.l1(state))
        a = F.relu(self.l2(a))
        mean = self.max_action * torch.tanh(self.l3_mean(a))
        sigma = F.softplus(self.l3_sigma(a)) + 0.001
        normal = Normal(mean, sigma)
        action1 = normal.rsample().clamp(-self.max_action, self.max_action)
        action2 = normal.rsample().clamp(-self.max_action, self.max_action)
        prob1 = normal.log_prob(action1).sum(1, keepdim=True)
        prob2 = normal.log_prob(action2).sum(1, keepdim=True)
        probm = normal.log_prob(mean).sum(1, keepdim=True)
        prob1 = torch.exp(prob1)
        prob2 = torch.exp(prob2)
        probm = torch.exp(probm)
        return action1, action2, mean, sigma, prob1, prob2, probm


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


def get_init_inputs():
    return [[], {'state_dim': 4, 'action_dim': 4, 'max_action': 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
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)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, 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 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_add_clamp_mul_softplus_tanh_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp5 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp6 = 20.0
    tmp7 = tmp5 > tmp6
    tmp8 = tl_math.exp(tmp5)
    tmp9 = libdevice.log1p(tmp8)
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tmp11 = 0.001
    tmp12 = tmp10 + tmp11
    tmp13 = tmp4 * tmp12
    tmp14 = tmp3 + tmp13
    tmp15 = -4.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = triton_helpers.minimum(tmp16, tmp2)
    tl.store(out_ptr0 + x0, tmp17, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (256, 4), (4, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (256, 256), (256, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (4, 256), (256, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 256), (256, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 256), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf0
        buf11 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(16384)](buf1,
            primals_2, buf11, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_4, (256, 256), (1, 256), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf2
        buf10 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(16384)](buf3,
            primals_5, buf10, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 256),
            (256, 1), 0), reinterpret_tensor(primals_6, (256, 4), (1, 256),
            0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 256),
            (256, 1), 0), reinterpret_tensor(primals_8, (256, 4), (1, 256),
            0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = torch.ops.aten.normal_functional.default(buf6)
        buf8 = buf7
        del buf7
        buf9 = buf6
        del buf6
        triton_poi_fused_add_clamp_mul_softplus_tanh_1[grid(256)](buf4,
            buf8, buf5, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 256), (256, 1), 0
        ), reinterpret_tensor(buf3, (64, 256), (256, 1), 0
        ), buf4, buf5, buf8, primals_8, primals_6, buf10, primals_4, buf11


class ActorNew(nn.Module):

    def __init__(self, state_dim, action_dim, max_action):
        super(ActorNew, self).__init__()
        self.l1 = nn.Linear(state_dim, 256)
        self.l2 = nn.Linear(256, 256)
        self.l3_sigma = nn.Linear(256, action_dim)
        self.l3_mean = nn.Linear(256, action_dim)
        self.max_action = max_action
        self.action_dim = action_dim
        self.state_dim = state_dim

    def forward_all(self, state, *args):
        a = F.relu(self.l1(state))
        a = F.relu(self.l2(a))
        mean = self.max_action * torch.tanh(self.l3_mean(a))
        sigma = F.softplus(self.l3_sigma(a)) + 0.001
        normal = Normal(mean, sigma)
        action = normal.rsample()
        log_prob = normal.log_prob(action).sum(1, keepdim=True)
        action = action.clamp(-self.max_action, self.max_action)
        return action, log_prob, mean, sigma

    def forward_all_sample(self, state, *args):
        a = F.relu(self.l1(state))
        a = F.relu(self.l2(a))
        mean = self.max_action * torch.tanh(self.l3_mean(a))
        sigma = F.softplus(self.l3_sigma(a)) + 0.001
        normal = Normal(mean, sigma)
        action1 = normal.rsample().clamp(-self.max_action, self.max_action)
        action2 = normal.rsample().clamp(-self.max_action, self.max_action)
        prob1 = normal.log_prob(action1).sum(1, keepdim=True)
        prob2 = normal.log_prob(action2).sum(1, keepdim=True)
        probm = normal.log_prob(mean).sum(1, keepdim=True)
        prob1 = torch.exp(prob1)
        prob2 = torch.exp(prob2)
        probm = torch.exp(probm)
        return action1, action2, mean, sigma, prob1, prob2, probm

    def forward(self, input_0):
        primals_1 = self.l1.weight
        primals_2 = self.l1.bias
        primals_4 = self.l2.weight
        primals_5 = self.l2.bias
        primals_6 = self.l3_sigma.weight
        primals_7 = self.l3_sigma.bias
        primals_8 = self.l3_mean.weight
        primals_9 = self.l3_mean.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])
        return output[0]
