class_docstr: The "ramp": Y(t) = (t+V) mod 1 - 0.5, where V is uniform over [0, 1].
docstr: Initializer.
  
  Args:
    index_points: 1-D Tensor representing the locations at which to evaluate
      the process.
    phase: Float in [0,1]. Specifies a realization of V.
    dtype: Data type of the returned realization. Defaults to `tf.float32`.
    validate_args: required by `Distribution` class but unused.
    allow_nan_stats: required by `Distribution` class but unused.
    name: String. Name of the created object.
code: def __init__(
      self,
      index_points,
      phase=None,
      dtype=tf.float32,
      validate_args=False,
      allow_nan_stats=True,
      name="ramp",
  ):
      """Initializer.
  
      Args:
        index_points: 1-D Tensor representing the locations at which to evaluate
          the process.
        phase: Float in [0,1]. Specifies a realization of V.
        dtype: Data type of the returned realization. Defaults to `tf.float32`.
        validate_args: required by `Distribution` class but unused.
        allow_nan_stats: required by `Distribution` class but unused.
        name: String. Name of the created object.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          self._index_points = tf.convert_to_tensor(
              index_points, dtype_hint=dtype, name="index_points"
          )
          self._phase = phase
      super().__init__(
          dtype=dtype,
          reparameterization_type=tfp.distributions.NOT_REPARAMETERIZED,
          validate_args=validate_args,
          allow_nan_stats=allow_nan_stats,
          parameters=parameters,
          name=name,
      )
  
file_path: compression/models/toy_sources/ramp.py
imports: import tensorflow
  import tensorflow_probability
label_desc: Use image node_id 1 to create a new Ramp object from inherited base classes: tfp.distributions with example: obj = Ramp(index_points, phase, dtype, validate_args, allow_nan_stats, name)