docstr: Instantiates the `Shift` bijector which computes `Y = g(X; shift) = X + shift`
  where `shift` is a numeric `Tensor`.
  
  Parameters
  ----------
  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 which computes `Y = g(X; shift) = X + shift`
      where `shift` is a numeric `Tensor`.
  
      Parameters
      ----------
      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.
      """
      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,
              name=name,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import warnings
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import quantized_distribution
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 1 to create a new Shift object from inherited base classes: bijector with example: obj = Shift(shift, validate_args, name)