class_docstr: Compute `Y = g(X; shift) = X + shift`.
  
  where `shift` is a numeric `Tensor`.
  
  Example Use:
  
  ```python
  shift = Shift([-1., 0., 1])
  x = [1., 2, 3]
  # `forward` is equivalent to:
  # y = x + shift
  y = shift.forward(x)  # [0., 2., 4.]
  ```
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docstr: Instantiates the `Shift` bijector.
  
  Args:
    shift: Floating-point `Tensor`.
    validate_args: Python `bool` indicating whether arguments should be
      checked for correctness.
    name: Python `str` name given to ops managed by this object.
docstr_tok: ["Instantiates","the","`","Shift","`","bijector",".","Args",":","shift",":","Floating-point","`","Tensor","`",".","validate_args",":","Python","`","bool","`","indicating","whether","arguments","should","be","checked","for","correctness",".","name",":","Python","`","str","`","name","given","to","ops","managed","by","this","object","."]
code: def __init__(self, shift, validate_args=False, name="shift"):
      """Instantiates the `Shift` bijector.
  
      Args:
        shift: Floating-point `Tensor`.
        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:
          dtype = dtype_util.common_dtype(
              [shift], dtype_hint=tf.float32
          )
          self._shift = tensor_util.convert_nonref_to_tensor(
              shift, dtype=dtype, name="shift"
          )
          super(Shift, self).__init__(
              forward_min_event_ndims=0,
              is_constant_jacobian=True,
              dtype=dtype,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
file_path: probability/tensorflow_probability/python/bijectors/shift.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 to create a new Shift object from inherited base classes: bijector, bijector with example: obj = Shift(shift, validate_args, name)