class_docstr: Implements a Truncated Normal distribution with location scaling.
  
  Location scaling prevents the location to be "too far" from 0, which ultimately
  leads to numerically unstable samples and poor gradient computation (e.g. gradient explosion).
  In practice, the location is computed according to
  
      .. math::
          loc = tanh(loc / upscale) * upscale.
  
  This behaviour can be disabled by switching off the tanh_loc parameter (see below).
  
  Parameters
  ----------
  loc:
      normal distribution location parameter
  scale:
      normal distribution sigma parameter (squared root of variance)
  min:
      minimum value of the distribution. Default = -1.0
  max:
      maximum value of the distribution. Default = 1.0
  upscale:
      scaling factor. Default = 5.0
  tanh_loc:
      if ``True``, the above formula is used for
      the location scaling, otherwise the raw value is kept.
      Default is ``False``
  
  References
  ----------
  - https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
  
  Notes
  -----
  This implementation is strongly based on:
      - https://github.com/pytorch/rl/blob/main/torchrl/modules/distributions/truncated_normal.py
      - https://github.com/toshas/torch_truncnorm
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code: def _from_std_rv(self, value):
      return value * self.scale + self.loc
  
imports: import math
  from numbers import Number
  from typing import Dict, Optional, Tuple, Union
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import broadcast_all
  import torch.nn.functional
  from .distribution_output import DistributionOutput
  from gluonts.core.component import validated
  from torch.distributions import Distribution
label_desc: Use image node_id 13 for calling the TruncatedNormal obj's underlying member method code with example usage: obj._from_std_rv(value) and returns: unknown