class_docstr: The Truncated Cauchy distribution.
  
  The truncated Cauchy is a Cauchy distribution bounded between `low`
  and `high` (the pdf is 0 outside these bounds and renormalized).
  
  Samples from this distribution are differentiable with respect to `loc`
  and `scale`, but not with respect to the bounds `low` and `high`.
  
  ### Mathematical Details
  
  The probability density function (pdf) of this distribution is:
  ```none
    pdf(x; loc, scale, low, high) =
        { 1 / (pi * scale * (1 + z**2) * A) for low <= x <= high
        { 0                                 otherwise
    z = (x - loc) / scale
    A = CauchyCDF((high - loc) / scale) - CauchyCDF((low - loc) / scale)
  ```
  
  where:
  
  * `CauchyCDF` is the cumulative density function of the Cauchy distribution
    with 0 mean and unit variance.
  
  This is a scalar distribution so the event shape is always scalar and the
  dimensions of the parameters define the batch_shape.
  
  #### Examples
  ```python
  
  tfd = tfp.distributions
  # Define a batch of two scalar TruncatedCauchy distributions with modes
  # at 0. and 1.0 .
  dist = tfd.TruncatedCauchy(loc=[0., 1.], scale=1.,
                             low=[-1., 0.],
                             high=[1., 1.])
  
  # Evaluate the pdf of the distributions at 0.5 and 0.8 respectively returning
  # a 2-vector tensor.
  dist.prob([0.5, 0.8])
  
  # Get 3 samples, returning a 3 x 2 tensor.
  dist.sample([3])
  ```
code: def _standardized_low_and_high(
      self, loc=None, scale=None, low=None, high=None
  ):
      loc, scale, low, high = self._loc_scale_low_high(
          loc=loc, scale=scale, low=low, high=high
      )
      return (low - loc) / scale, (high - loc) / scale
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import numeric
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 3 for calling the TruncatedCauchy obj's underlying member method code with example usage: obj._standardized_low_and_high(loc, scale, low, high) and returns: unknown,  unknown