class_docstr: Utility distribution to cast inputs/outputs of another distribution.
class_docstr_tok: ["Utility","distribution","to","cast","inputs\/outputs","of","another","distribution","."]
code: def __init__(self, distribution, dtype):
      parameters = dict(locals())
      name = "CastTo{}".format(dtype_util.name(dtype))
      with tf.name_scope(name) as name:
          self._distribution = distribution
          self._dtype = dtype
          super(_NonCompositeTensorCast, self).__init__(
              dtype=dtype,
              validate_args=distribution.validate_args,
              allow_nan_stats=distribution.allow_nan_stats,
              reparameterization_type=distribution.reparameterization_type,
              parameters=parameters,
              name=name,
          )
  
imports: import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 1 to create a new _NonCompositeTensorCast object from inherited base classes: distribution_lib with example: obj = _NonCompositeTensorCast(distribution, dtype)