class_docstr: A distribution induced by applying a tanh transformation to a Gaussian random variable.
  
  Algorithms like SAC and Pearl use this transformed distribution.
  It can be thought of as a distribution of X where
      :math:`Y ~ \mathcal{N}(\mu, \sigma)`
      :math:`X = tanh(Y)`
  
  Args:
      loc (torch.Tensor): The mean of this distribution.
      scale (torch.Tensor): The stdev of this distribution.
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code: def __init__(self, loc, scale):
      self._normal = Independent(Normal(loc, scale), 1)
      super().__init__()
  
file_path: garage/src/garage/torch/distributions/tanh_normal.py
imports: import torch
  from torch.distributions import Normal
  from torch.distributions.independent import Independent
label_desc: Use image node_id 1 to create a new TanhNormal object from inherited base classes: torch.distributions with example: obj = TanhNormal(loc, scale)