class_docstr: Event space bijector for Masked distributions.
class_docstr_tok: ["Event","space","bijector","for","Masked","distributions","."]
code: def __init__(self, masked, underlying_bijector):
      self._masked = masked
      self._bijector = underlying_bijector
      super(_NonCompositeTensorMaskedBijector, self).__init__(
          validate_args=underlying_bijector.validate_args,
          dtype=underlying_bijector.dtype,
          forward_min_event_ndims=underlying_bijector.forward_min_event_ndims,
          inverse_min_event_ndims=underlying_bijector.inverse_min_event_ndims,
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.distributions import batch_broadcast
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 to create a new _NonCompositeTensorMaskedBijector object from inherited base classes: bijector_lib with example: obj = _NonCompositeTensorMaskedBijector(masked, underlying_bijector)