class_docstr: Event space bijector for Masked distributions.
class_docstr_tok: ["Event","space","bijector","for","Masked","distributions","."]
docstr: Maybe return a `_NonCompositeTensorMaskedBijector`.
docstr_tok: ["Maybe","return","a","`","_NonCompositeTensorMaskedBijector","`","."]
code: def __new__(cls, *args, **kwargs):
      """Maybe return a `_NonCompositeTensorMaskedBijector`."""
  
      if cls is _MaskedBijector:
          if args:
              masked = args[0]
          else:
              masked = kwargs.get("masked")
          if len(args) > 1:
              bijector = args[1]
          else:
              bijector = kwargs.get("underlying_bijector")
  
          if not (
              auto_composite_tensor.is_composite_tensor(masked)
              and auto_composite_tensor.is_composite_tensor(bijector)
          ):
              return _NonCompositeTensorMaskedBijector(*args, **kwargs)
      return super(_MaskedBijector, cls).__new__(cls)
  
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 for calling the _MaskedBijector obj's underlying member method code with example usage: obj.__new__(cls) and returns: super, _NonCompositeTensorMaskedBijector