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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docstr: Returns the icdf function evaluated at `value`.
  
  Returns the icdf function evaluated at `value` on the underlying
  normal distribution.
  
  Args:
      value (torch.Tensor): The element where the cdf is being evaluated
          at.
  
  Returns:
      torch.Tensor: the result of the cdf being computed.
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code: def icdf(self, value):
      """Returns the icdf function evaluated at `value`.
  
      Returns the icdf function evaluated at `value` on the underlying
      normal distribution.
  
      Args:
          value (torch.Tensor): The element where the cdf is being evaluated
              at.
  
      Returns:
          torch.Tensor: the result of the cdf being computed.
  
      """
      return self._normal.icdf(value)
  
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 7 for calling the TanhNormal obj's underlying member method code with example usage: obj.icdf(value) and returns: self