import torch
from torch import nn


class ELBO(nn.Module):

    def __init__(self, reconstruction: nn.Module):
        super().__init__()
        self.reconstruction = reconstruction

    def forward(self, input: torch.Tensor, target: torch.Tensor, mu: torch.
        Tensor, log_var: torch.Tensor) ->torch.autograd.Variable:
        reconstruction = self.reconstruction(input, target)
        kl = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
        return kl + reconstruction

e3f1ed36ac90795bea42cb8d5f2a14b43834f11a
<mask token>


class ELBO(nn.Module):
    <mask token>
    <mask token>

<mask token>


class ELBO(nn.Module):

    def __init__(self, reconstruction: nn.Module):
        super().__init__()
        self.reconstruction = reconstruction
    <mask token>

<mask token>


class ELBO(nn.Module):

    def __init__(self, reconstruction: nn.Module):
        super().__init__()
        self.reconstruction = reconstruction

    def forward(self, input: torch.Tensor, target: torch.Tensor, mu: torch.
        Tensor, log_var: torch.Tensor) ->torch.autograd.Variable:
        reconstruction = self.reconstruction(input, target)
        kl = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
        return kl + reconstruction

import torch
from torch import nn


class ELBO(nn.Module):

    def __init__(self, reconstruction: nn.Module):
        super().__init__()
        self.reconstruction = reconstruction

    def forward(self, input: torch.Tensor, target: torch.Tensor, mu: torch.
        Tensor, log_var: torch.Tensor) ->torch.autograd.Variable:
        reconstruction = self.reconstruction(input, target)
        kl = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
        return kl + reconstruction
