Add losses for VAE

  batch_size = 2,

  hidden_space = 2,

  num of original attribute = 3

  >>> import numpy as np

  >>> import torch

  >>> import tensorflow as tf

  >>> logvar = np.array([[1.0,1.3],[0.6,1.2]])

  >>> mu = np.array([[0.2,0.7],[1.2,0.4]])

  >>> x = np.array([[0.9,0.4,0.8],[0.3,0,1]])

  >>> reconstruction = np.array([[0.8,0.3,0.7],[0.2,0,0.9]])

  >>> reconstruction_x = np.array([[0.8,0.3,0.7],[0.2,0,0.9]])

  Case tensorflow

  >>> VAE_ELBO()._compute_tf_loss(tf.constant(logvar), tf.constant(mu), tf.constant(x), tf.constant(reconstruction_x))

  batch_size = 2,

  hidden_space = 2,

  >>> import numpy as np

  >>> import torch

  >>> import tensorflow as tf

  >>> logvar = np.array([[1.0,1.3],[0.6,1.2]])

  >>> mu = np.array([[0.2,0.7],[1.2,0.4]])

  Case tensorflow

  >>> VAE_KLDivergence()._compute_tf_loss(tf.constant(logvar), tf.constant(mu))

  tf.Tensor([0.52783368 0.24813068], shape=(2,), dtype=float64)

  <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.17381787, 0.51425203])>

  Case pytorch

  >>> (VAE_KLDivergence()._create_pytorch_loss())(torch.tensor(logvar), torch.tensor(mu))

  batch_size = 2,

  num_of variable = variable,

  >>> import numpy as np

  >>> import torch

  >>> import tensorflow as tf

  >>> inputs = np.array([[0.7,0.3],[0.9,0.1]])

  Case tensorflow

  >>> ShannonEntropy()._compute_tf_loss(tf.constant(inputs))

  tf.Tensor([0.52783368 0.24813068], shape=(2,), dtype=float64)

  <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.30543215, 0.16254149])>

  Case pytorch

  >>> (ShannonEntropy()._create_pytorch_loss())(torch.tensor(inputs))

  tensor([0.1738, 0.5143], dtype=torch.float64)

  tensor([0.3054, 0.1625], dtype=torch.float64)

  """

  def _compute_tf_loss(self, inputs):