class_docstr: Compute `Y = g(X) = exp(X)`.
  
  Example Use:
  
  ```python
  # Create the Y=g(X)=exp(X) transform which works only on Tensors with 1
  # batch ndim 2.
  exp = Exp()
  x = [[[1., 2],
         [3, 4]],
        [[5, 6],
         [7, 8]]]
  exp(x) == exp.forward(x)
  log(x) == exp.inverse(x)
  ```
  
  Note: the exp(.) is applied element-wise but the Jacobian is a reduction
  over the event space.
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docstr: Instantiates the `Exp` bijector.
  
  Args:
    validate_args: Python `bool` indicating whether arguments should be
      checked for correctness.
    name: Python `str` name given to ops managed by this object.
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code: def __init__(self, validate_args=False, name="exp"):
      """Instantiates the `Exp` bijector.
  
      Args:
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
      """
      # forward_min_event_ndims = 0.
      # No forward_min_event_ndims specified as this is done in PowerTransform.
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          super(Exp, self).__init__(
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
file_path: probability/tensorflow_probability/python/bijectors/exp.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import power_transform
label_desc: Use image node_id 1 to create a new Exp object from inherited base classes: power_transform with example: obj = Exp(validate_args, name)