# 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_6/inductor_cache/2q/c2qo3gh3y46foffuo4deaunecv5aknydoaksonjqz2rv3jg72afs.py
# Topologically Sorted Source Nodes: [pow_1], Original ATen: [aten.pow]
# Source node to ATen node mapping:
#   pow_1 => pow_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_3, 2), kwargs = {})
triton_poi_fused_pow_0 = async_compile.triton('triton_poi_fused_pow_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: '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_pow_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_pow_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0 * tmp0
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/u2/cu2bwgbqtjqj3753ql4tajeks6o4dwkkjzmvlm7utj2tgt7ldihs.py
# Topologically Sorted Source Nodes: [exp, pow_2], Original ATen: [aten.exp, aten.pow]
# Source node to ATen node mapping:
#   exp => exp
#   pow_2 => pow_2
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%primals_4,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%exp, 2), kwargs = {})
triton_poi_fused_exp_pow_1 = async_compile.triton('triton_poi_fused_exp_pow_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=[16], 
    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_exp_pow_1', '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_exp_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = tmp1 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/23/c23hjwmn6dk5yetvs6i5u2ugpycc3ttuknrvp2vcu7zhbwozz7n2.py
# Topologically Sorted Source Nodes: [add, act_sigma, mul, add_1], Original ATen: [aten.add, aten.sqrt, aten.mul]
# Source node to ATen node mapping:
#   act_sigma => sqrt
#   add => add
#   add_1 => add_1
#   mul => mul
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, 1e-08), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, %randn), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %mul), kwargs = {})
triton_poi_fused_add_mul_sqrt_2 = async_compile.triton('triton_poi_fused_add_mul_sqrt_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=[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_mul_sqrt_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_mul_sqrt_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp7 = tl.load(in_ptr2 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = 1e-08
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp8 = tmp6 * tmp7
    tmp9 = tmp2 + tmp8
    tl.store(in_out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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))
    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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1], Original ATen: [aten.pow]
        stream0 = get_raw_stream(0)
        triton_poi_fused_pow_0.run(primals_3, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [exp, pow_2], Original ATen: [aten.exp, aten.pow]
        triton_poi_fused_exp_pow_1.run(primals_4, buf2, 16, grid=grid(16), stream=stream0)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf2, (4, 4), (1, 4), 0), out=buf3)
        del buf2
        # Topologically Sorted Source Nodes: [epsilon], Original ATen: [aten.randn_like]
        buf4 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, act_sigma, mul, add_1], Original ATen: [aten.add, aten.sqrt, aten.mul]
        triton_poi_fused_add_mul_sqrt_2.run(buf6, primals_2, buf3, buf5, 256, grid=grid(256), stream=stream0)
        del primals_2
    return (buf6, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, buf5, )


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)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 math
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import init


def calculate_kl(mu_p, sig_p, mu_q, sig_q):
    """
    Calculates the Kullback-Leibler divergence between two univariate Gaussians (p and q)

    Args:
        mu_p: mean of the Gaussian p
        sig_p: standard deviation of the Gaussian p
        mu_q: mean of the Gaussian q
        sig_q: standard deviation of the Gaussian q
    """
    kl = 0.5 * (2 * torch.log(sig_p / sig_q) - 1 + (sig_q / sig_p).pow(2) +
        ((mu_p - mu_q) / sig_p).pow(2)).sum()
    return kl


class BayesLinear(nn.Module):
    """
    This class implements a Bayesian Linear layer, which has a distribution instead of weights. 
    """

    def __init__(self, in_features, out_features, bias=True,
        log_sigma_prior=-5, mu_prior=-1):
        """
        Initializes a BayesLinear layer. 

        Args:
            in_features: number of input features
            out_features: number of output features
            bias: whether to add bias  
            log_sigma_prior: the initial value of the standard deviation of the distribution
            mu_prior: the initial value of the mean of the distribution
        """
        super(BayesLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.w_mu = nn.Parameter(torch.Tensor(out_features, in_features))
        self.w_log_sigma = nn.Parameter(torch.Tensor(out_features, in_features)
            )
        self.mu_prior_init = mu_prior
        self.log_sigma_prior_init = log_sigma_prior
        if bias is True:
            self.bias = nn.Parameter(torch.Tensor(out_features))
        self.reset_parameters()

    def reset_parameters(self):
        """
        Resets the parameters of the layer
        """
        init.kaiming_uniform_(self.w_mu, a=math.sqrt(5))
        init.uniform_(self.w_log_sigma, self.log_sigma_prior_init - 0.1,
            self.log_sigma_prior_init)
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.w_mu)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        """
        Performs a forward pass of the input. Uses the Reparemetrization trick proposed by Kingma et al. 
        in "Variational Dropout and the Local Reparameterization trick" to sample directly from the activations.
        
        Args:
            input: the input to be forwarded
        """
        act_mu = F.linear(input, self.w_mu, self.bias)
        act_sigma = torch.sqrt(F.linear(input ** 2, torch.exp(self.
            w_log_sigma) ** 2) + 1e-08)
        epsilon = torch.randn_like(act_mu)
        return act_mu + act_sigma * epsilon

    def kl(self):
        """
        Returns the Kullback-Leibler divergence between the prior and the posterior of Bayesian layer.
        """
        return calculate_kl(torch.Tensor([self.mu_prior_init]), torch.exp(
            torch.Tensor([self.log_sigma_prior_init])), self.w_mu, torch.
            exp(self.w_log_sigma))


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


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

import torch
from torch import device
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.triton_helpers import libdevice, math as tl_math
import math
import torch.nn as nn
from torch.nn import init
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_pow_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0 * tmp0
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_exp_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = tmp1 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_sqrt_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    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')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp7 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = 1e-08
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp8 = tmp6 * tmp7
    tmp9 = tmp2 + tmp8
    tl.store(in_out_ptr0 + x2, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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))
    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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_0[grid(256)](primals_3, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_exp_pow_1[grid(16)](primals_4, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf2, (4, 4), (1, 4), 0), out=buf3)
        del buf2
        buf4 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_mul_sqrt_2[grid(256)](buf6, primals_2, buf3,
            buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf6, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, buf5


def calculate_kl(mu_p, sig_p, mu_q, sig_q):
    """
    Calculates the Kullback-Leibler divergence between two univariate Gaussians (p and q)

    Args:
        mu_p: mean of the Gaussian p
        sig_p: standard deviation of the Gaussian p
        mu_q: mean of the Gaussian q
        sig_q: standard deviation of the Gaussian q
    """
    kl = 0.5 * (2 * torch.log(sig_p / sig_q) - 1 + (sig_q / sig_p).pow(2) +
        ((mu_p - mu_q) / sig_p).pow(2)).sum()
    return kl


class BayesLinearNew(nn.Module):
    """
    This class implements a Bayesian Linear layer, which has a distribution instead of weights. 
    """

    def __init__(self, in_features, out_features, bias=True,
        log_sigma_prior=-5, mu_prior=-1):
        """
        Initializes a BayesLinear layer. 

        Args:
            in_features: number of input features
            out_features: number of output features
            bias: whether to add bias  
            log_sigma_prior: the initial value of the standard deviation of the distribution
            mu_prior: the initial value of the mean of the distribution
        """
        super(BayesLinearNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.w_mu = nn.Parameter(torch.Tensor(out_features, in_features))
        self.w_log_sigma = nn.Parameter(torch.Tensor(out_features, in_features)
            )
        self.mu_prior_init = mu_prior
        self.log_sigma_prior_init = log_sigma_prior
        if bias is True:
            self.bias = nn.Parameter(torch.Tensor(out_features))
        self.reset_parameters()

    def reset_parameters(self):
        """
        Resets the parameters of the layer
        """
        init.kaiming_uniform_(self.w_mu, a=math.sqrt(5))
        init.uniform_(self.w_log_sigma, self.log_sigma_prior_init - 0.1,
            self.log_sigma_prior_init)
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.w_mu)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def kl(self):
        """
        Returns the Kullback-Leibler divergence between the prior and the posterior of Bayesian layer.
        """
        return calculate_kl(torch.Tensor([self.mu_prior_init]), torch.exp(
            torch.Tensor([self.log_sigma_prior_init])), self.w_mu, torch.
            exp(self.w_log_sigma))

    def forward(self, input_0):
        primals_1 = self.w_mu
        primals_4 = self.w_log_sigma
        primals_2 = self.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
