class_docstr: Compute Y = g(X) = X.
  
  Example Use:
  
  ```python
  # Create the Y=g(X)=X transform which is intended for Tensors with 1 batch
  # ndim and 1 event ndim (i.e., vector of vectors).
  identity = Identity()
  x = [[1., 2],
       [3, 4]]
  x == identity.forward(x) == identity.inverse(x)
  ```
code: def __init__(self, validate_args=False, name="identity"):
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          super(Identity, self).__init__(
              forward_min_event_ndims=0,
              is_constant_jacobian=True,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
      # Override superclass private fields to eliminate caching, avoiding a memory
      # leak caused by the `y is x` characteristic of this bijector.
      self._from_x = self._from_y = _NoOpCache()
  
file_path: probability/tensorflow_probability/python/bijectors/identity.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
label_desc: Use image node_id 1 to create a new Identity object from inherited base classes: bijector, bijector with example: obj = Identity(validate_args, name)