class_docstr: Compute `g(X) = X ** power`; where X is a non-negative real number.
  
  When `power` is an odd integer, this bijector has domain the whole real line,
  and otherwise is constrained to non-negative numbers.
  
  Note: Powers that are reciprocal of odd integers like `1. / 3` are not
  supported because of numerical precision issues that make this property
  difficult to test. In order to simulate this behavior, we recommend using
  the `Invert` bijector instead (i.e. instead of `tfb.Power(power=1./3)`
  use `tfb.Invert(tfb.Power(power=3.))`).
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docstr: Instantiates the `Power` bijector.
  
  Args:
    power: float `Tensor` power to raise the input to.
    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, power, validate_args=False, name="power"):
      """Instantiates the `Power` bijector.
  
      Args:
        power: float `Tensor` power to raise the input to.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          self._power = tensor_util.convert_nonref_to_tensor(
              power, name="power", dtype_hint=tf.float32
          )
          super(Power, self).__init__(
              forward_min_event_ndims=0,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
file_path: probability/tensorflow_probability/python/bijectors/power.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 to create a new Power object from inherited base classes: bijector, bijector with example: obj = Power(power, validate_args, name)