Fixed Formatting

    import torch

    return torch.nn.L1Loss(reduction='none')

class HuberLoss(Loss):

  """Modified version of L1 Loss, also known as Smooth L1 loss. Less sensitive to small errors, linear for larger errors."""

  def _compute_tf_loss(self, output, labels):

    import tensorflow as tf

    output, labels = _make_tf_shapes_consistent(output, labels)

    import torch

    return torch.nn.SmoothL1Loss(reduction='none')

class L2Loss(Loss):

  """The squared difference between the true and predicted values."""

  """The KL_divergence between hidden distribution and normal distribution.

  This loss represents KL divergence losses between normal distribution(using parameter of distribution)

  based on  _[1].z

  based on  _[1].

  The logvar should have shape (batch_size, hidden_space) and each term represents

  standard deviation of hidden distribution. The mean shuold have