class_docstr: Bijector that computes `Y = tanh(X)`, therefore `Y in (-1, 1)`.
  
  This can be achieved by an affine transform of the Sigmoid bijector, i.e.,
  it is equivalent to
  ```
  tfb.Chain([tfb.Shift(shift=-1.),
             tfb.Scale(scale=2.),
             tfb.Sigmoid(),
             tfb.Scale(scale=2.)])
  ```
  
  However, using the `Tanh` bijector directly is slightly faster and more
  numerically stable.
code: def __init__(self, validate_args=False, name="tanh"):
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          super(Tanh, self).__init__(
              forward_min_event_ndims=0,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
file_path: probability/tensorflow_probability/python/bijectors/tanh.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
label_desc: Use image node_id 1 to create a new Tanh object from inherited base classes: bijector, bijector with example: obj = Tanh(validate_args, name)