# 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_1 : [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/s5/cs5zzpz6m6kai3axr7c2ij4mngrlsrjl6xdg3ukojun5qp6ky3r5.py
# Topologically Sorted Source Nodes: [log_std, add, mul, log_std_1, std, mul_1, z, action, sub], Original ATen: [aten.tanh, aten.add, aten.mul, aten.exp, aten.sub]
# Source node to ATen node mapping:
#   action => tanh_1
#   add => add
#   log_std => tanh
#   log_std_1 => add_1
#   mul => mul
#   mul_1 => mul_1
#   std => exp
#   sub => sub
#   z => add_2
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_7,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 11.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, -20), kwargs = {})
#   %exp : [num_users=4] = call_function[target=torch.ops.aten.exp.default](args = (%add_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%normal_functional, %exp), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %mul_1), kwargs = {})
#   %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %view_5), kwargs = {})
triton_poi_fused_add_exp_mul_sub_tanh_1 = async_compile.triton('triton_poi_fused_add_exp_mul_sub_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: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_mul_sub_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_exp_mul_sub_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, 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)
    tmp9 = tl.load(in_ptr1 + (x0), xmask)
    tmp10 = tl.load(in_ptr2 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tmp4 = 11.0
    tmp5 = tmp3 * tmp4
    tmp6 = -20.0
    tmp7 = tmp5 + tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp11 = tmp10 * tmp8
    tmp12 = tmp9 + tmp11
    tmp13 = tmp12 - tmp9
    tmp14 = libdevice.tanh(tmp12)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp12, xmask)
    tl.store(out_ptr2 + (x0), tmp13, xmask)
    tl.store(out_ptr3 + (x0), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6r/c6rdrkmlczh5g4bcyxaso75xz5huarnchrjyckq264jw7cieitql.py
# Topologically Sorted Source Nodes: [var, log_scale, pow_2, neg, mul_2, truediv, sub_1, sub_2, pow_3, sub_3, add_3, log_1, log_prob, log_prob_1], Original ATen: [aten.pow, aten.log, aten.neg, aten.mul, aten.div, aten.sub, aten.rsub, aten.add, aten.sum]
# Source node to ATen node mapping:
#   add_3 => add_3
#   log_1 => log_1
#   log_prob => sub_4
#   log_prob_1 => sum_1
#   log_scale => log
#   mul_2 => mul_2
#   neg => neg
#   pow_2 => pow_2
#   pow_3 => pow_3
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   truediv => div
#   var => pow_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%exp, 2), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%exp,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%pow_2,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, %mul_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %log), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, 0.9189385332046727), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%tanh_1, 2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %pow_3), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_3, 1e-06), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_3,), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %log_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_4, [-1], True), kwargs = {})
triton_poi_fused_add_div_log_mul_neg_pow_rsub_sub_sum_2 = async_compile.triton('triton_poi_fused_add_div_log_mul_neg_pow_rsub_sub_sum_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=[64], 
    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_div_log_mul_neg_pow_rsub_sub_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_log_mul_neg_pow_rsub_sub_sum_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = -tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = 2.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 / tmp6
    tmp8 = tl_math.log(tmp3)
    tmp9 = tmp7 - tmp8
    tmp10 = 0.9189385332046727
    tmp11 = tmp9 - tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = 1.0
    tmp15 = tmp14 - tmp13
    tmp16 = 1e-06
    tmp17 = tmp15 + tmp16
    tmp18 = tl_math.log(tmp17)
    tmp19 = tmp11 - tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = -tmp21
    tmp24 = tmp23 * tmp23
    tmp25 = tmp24 * tmp5
    tmp26 = tmp22 / tmp25
    tmp27 = tl_math.log(tmp23)
    tmp28 = tmp26 - tmp27
    tmp29 = tmp28 - tmp10
    tmp31 = tmp30 * tmp30
    tmp32 = tmp14 - tmp31
    tmp33 = tmp32 + tmp16
    tmp34 = tl_math.log(tmp33)
    tmp35 = tmp29 - tmp34
    tmp36 = tmp19 + tmp35
    tmp38 = tmp37 * tmp37
    tmp39 = -tmp38
    tmp41 = tmp40 * tmp40
    tmp42 = tmp41 * tmp5
    tmp43 = tmp39 / tmp42
    tmp44 = tl_math.log(tmp40)
    tmp45 = tmp43 - tmp44
    tmp46 = tmp45 - tmp10
    tmp48 = tmp47 * tmp47
    tmp49 = tmp14 - tmp48
    tmp50 = tmp49 + tmp16
    tmp51 = tl_math.log(tmp50)
    tmp52 = tmp46 - tmp51
    tmp53 = tmp36 + tmp52
    tmp55 = tmp54 * tmp54
    tmp56 = -tmp55
    tmp58 = tmp57 * tmp57
    tmp59 = tmp58 * tmp5
    tmp60 = tmp56 / tmp59
    tmp61 = tl_math.log(tmp57)
    tmp62 = tmp60 - tmp61
    tmp63 = tmp62 - tmp10
    tmp65 = tmp64 * tmp64
    tmp66 = tmp14 - tmp65
    tmp67 = tmp66 + tmp16
    tmp68 = tl_math.log(tmp67)
    tmp69 = tmp63 - tmp68
    tmp70 = tmp53 + tmp69
    tl.store(out_ptr0 + (x0), tmp70, 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, (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, ))
    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
        buf15 = 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, buf15, 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
        buf14 = 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, buf14, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mu], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 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, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.normal_functional]
        buf8 = torch.ops.aten.normal_functional.default(buf7)
        buf9 = buf8
        del buf8
        buf6 = buf7; del buf7  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_std, add, mul, log_std_1, std, mul_1, z, action, sub], Original ATen: [aten.tanh, aten.add, aten.mul, aten.exp, aten.sub]
        triton_poi_fused_add_exp_mul_sub_tanh_1.run(buf5, buf4, buf9, buf6, buf10, buf12, buf11, 256, grid=grid(256), stream=stream0)
        buf13 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [var, log_scale, pow_2, neg, mul_2, truediv, sub_1, sub_2, pow_3, sub_3, add_3, log_1, log_prob, log_prob_1], Original ATen: [aten.pow, aten.log, aten.neg, aten.mul, aten.div, aten.sub, aten.rsub, aten.add, aten.sum]
        triton_poi_fused_add_div_log_mul_neg_pow_rsub_sub_sum_2.run(buf12, buf6, buf11, buf13, 64, grid=grid(64), stream=stream0)
    return (buf11, buf13, buf10, reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf6, 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), buf5, buf6, buf9, buf11, buf12, primals_8, primals_6, buf14, primals_4, buf15, )


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)
    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
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable
from torch.distributions import Normal


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 GaussianDist(MLP):
    """Multilayer perceptron with Gaussian distribution output.

    Attributes:
        mu_activation (function): bounding function for mean
        log_std_min (float): lower bound of log std
        log_std_max (float): upper bound of log std
        mu_layer (nn.Linear): output layer for mean
        log_std_layer (nn.Linear): output layer for log std
    """

    def __init__(self, input_size: 'int', output_size: 'int', hidden_sizes:
        'list', hidden_activation: 'Callable'=F.relu, mu_activation:
        'Callable'=torch.tanh, log_std_min: 'float'=-20, log_std_max:
        'float'=2, init_fn: 'Callable'=init_layer_uniform):
        """Initialize."""
        super(GaussianDist, self).__init__(input_size=input_size,
            output_size=output_size, hidden_sizes=hidden_sizes,
            hidden_activation=hidden_activation, use_output_layer=False)
        self.mu_activation = mu_activation
        self.log_std_min = log_std_min
        self.log_std_max = log_std_max
        in_size = hidden_sizes[-1]
        self.log_std_layer = nn.Linear(in_size, output_size)
        self.log_std_layer = init_fn(self.log_std_layer)
        self.mu_layer = nn.Linear(in_size, output_size)
        self.mu_layer = init_fn(self.mu_layer)

    def get_dist_params(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, ...]:
        """Return gausian distribution parameters."""
        hidden = super(GaussianDist, self).forward(x)
        mu = self.mu_activation(self.mu_layer(hidden))
        log_std = torch.tanh(self.log_std_layer(hidden))
        log_std = self.log_std_min + 0.5 * (self.log_std_max - self.log_std_min
            ) * (log_std + 1)
        std = torch.exp(log_std)
        return mu, log_std, std

    def forward(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, ...]:
        """Forward method implementation."""
        mu, _, std = self.get_dist_params(x)
        dist = Normal(mu, std)
        action = dist.sample()
        return action, dist


class TanhGaussianDistParams(GaussianDist):
    """Multilayer perceptron with Gaussian distribution output."""

    def __init__(self, **kwargs):
        """Initialize."""
        super(TanhGaussianDistParams, self).__init__(**kwargs,
            mu_activation=identity)

    def forward(self, x: 'torch.Tensor', epsilon: 'float'=1e-06) ->Tuple[
        torch.Tensor, ...]:
        """Forward method implementation."""
        mu, _, std = super(TanhGaussianDistParams, self).get_dist_params(x)
        dist = Normal(mu, std)
        z = dist.rsample()
        action = torch.tanh(z)
        log_prob = dist.log_prob(z) - torch.log(1 - action.pow(2) + epsilon)
        log_prob = log_prob.sum(-1, keepdim=True)
        return action, log_prob, z, mu, std


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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable
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):
    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_exp_mul_sub_tanh_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, out_ptr3, 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)
    tmp9 = tl.load(in_ptr1 + x0, xmask)
    tmp10 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tmp4 = 11.0
    tmp5 = tmp3 * tmp4
    tmp6 = -20.0
    tmp7 = tmp5 + tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp11 = tmp10 * tmp8
    tmp12 = tmp9 + tmp11
    tmp13 = tmp12 - tmp9
    tmp14 = libdevice.tanh(tmp12)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp12, xmask)
    tl.store(out_ptr2 + x0, tmp13, xmask)
    tl.store(out_ptr3 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused_add_div_log_mul_neg_pow_rsub_sub_sum_2(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp37 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp40 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp47 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp54 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp57 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp64 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tmp0 * tmp0
    tmp2 = -tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = 2.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 / tmp6
    tmp8 = tl_math.log(tmp3)
    tmp9 = tmp7 - tmp8
    tmp10 = 0.9189385332046727
    tmp11 = tmp9 - tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = 1.0
    tmp15 = tmp14 - tmp13
    tmp16 = 1e-06
    tmp17 = tmp15 + tmp16
    tmp18 = tl_math.log(tmp17)
    tmp19 = tmp11 - tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = -tmp21
    tmp24 = tmp23 * tmp23
    tmp25 = tmp24 * tmp5
    tmp26 = tmp22 / tmp25
    tmp27 = tl_math.log(tmp23)
    tmp28 = tmp26 - tmp27
    tmp29 = tmp28 - tmp10
    tmp31 = tmp30 * tmp30
    tmp32 = tmp14 - tmp31
    tmp33 = tmp32 + tmp16
    tmp34 = tl_math.log(tmp33)
    tmp35 = tmp29 - tmp34
    tmp36 = tmp19 + tmp35
    tmp38 = tmp37 * tmp37
    tmp39 = -tmp38
    tmp41 = tmp40 * tmp40
    tmp42 = tmp41 * tmp5
    tmp43 = tmp39 / tmp42
    tmp44 = tl_math.log(tmp40)
    tmp45 = tmp43 - tmp44
    tmp46 = tmp45 - tmp10
    tmp48 = tmp47 * tmp47
    tmp49 = tmp14 - tmp48
    tmp50 = tmp49 + tmp16
    tmp51 = tl_math.log(tmp50)
    tmp52 = tmp46 - tmp51
    tmp53 = tmp36 + tmp52
    tmp55 = tmp54 * tmp54
    tmp56 = -tmp55
    tmp58 = tmp57 * tmp57
    tmp59 = tmp58 * tmp5
    tmp60 = tmp56 / tmp59
    tmp61 = tl_math.log(tmp57)
    tmp62 = tmp60 - tmp61
    tmp63 = tmp62 - tmp10
    tmp65 = tmp64 * tmp64
    tmp66 = tmp14 - tmp65
    tmp67 = tmp66 + tmp16
    tmp68 = tl_math.log(tmp67)
    tmp69 = tmp63 - tmp68
    tmp70 = tmp53 + tmp69
    tl.store(out_ptr0 + x0, tmp70, 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, (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,))
    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
        buf15 = 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, buf15, 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
        buf14 = 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, buf14, 256, 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, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 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, 4), (
            4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf8 = torch.ops.aten.normal_functional.default(buf7)
        buf9 = buf8
        del buf8
        buf6 = buf7
        del buf7
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_exp_mul_sub_tanh_1[grid(256)](buf5, buf4, buf9,
            buf6, buf10, buf12, buf11, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf13 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_add_div_log_mul_neg_pow_rsub_sub_sum_2[grid(64)](buf12
            , buf6, buf11, buf13, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return (buf11, buf13, buf10, reinterpret_tensor(buf4, (4, 4, 4, 4), (64,
        16, 4, 1), 0), buf6, 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), buf5, buf6, buf9,
        buf11, buf12, primals_8, primals_6, buf14, primals_4, buf15)


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 GaussianDist(MLP):
    """Multilayer perceptron with Gaussian distribution output.

    Attributes:
        mu_activation (function): bounding function for mean
        log_std_min (float): lower bound of log std
        log_std_max (float): upper bound of log std
        mu_layer (nn.Linear): output layer for mean
        log_std_layer (nn.Linear): output layer for log std
    """

    def __init__(self, input_size: 'int', output_size: 'int', hidden_sizes:
        'list', hidden_activation: 'Callable'=F.relu, mu_activation:
        'Callable'=torch.tanh, log_std_min: 'float'=-20, log_std_max:
        'float'=2, init_fn: 'Callable'=init_layer_uniform):
        """Initialize."""
        super(GaussianDist, self).__init__(input_size=input_size,
            output_size=output_size, hidden_sizes=hidden_sizes,
            hidden_activation=hidden_activation, use_output_layer=False)
        self.mu_activation = mu_activation
        self.log_std_min = log_std_min
        self.log_std_max = log_std_max
        in_size = hidden_sizes[-1]
        self.log_std_layer = nn.Linear(in_size, output_size)
        self.log_std_layer = init_fn(self.log_std_layer)
        self.mu_layer = nn.Linear(in_size, output_size)
        self.mu_layer = init_fn(self.mu_layer)

    def get_dist_params(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, ...]:
        """Return gausian distribution parameters."""
        hidden = super(GaussianDist, self).forward(x)
        mu = self.mu_activation(self.mu_layer(hidden))
        log_std = torch.tanh(self.log_std_layer(hidden))
        log_std = self.log_std_min + 0.5 * (self.log_std_max - self.log_std_min
            ) * (log_std + 1)
        std = torch.exp(log_std)
        return mu, log_std, std

    def forward(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, ...]:
        """Forward method implementation."""
        mu, _, std = self.get_dist_params(x)
        dist = Normal(mu, std)
        action = dist.sample()
        return action, dist


class TanhGaussianDistParamsNew(GaussianDist):
    """Multilayer perceptron with Gaussian distribution output."""

    def __init__(self, **kwargs):
        """Initialize."""
        super(TanhGaussianDistParamsNew, self).__init__(**kwargs,
            mu_activation=identity)

    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.log_std_layer.weight
        primals_7 = self.log_std_layer.bias
        primals_8 = self.mu_layer.weight
        primals_9 = self.mu_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])
        return output[0], output[1], output[2], output[3], output[4]
