docstr: Maybe return a non-`CompositeTensor` `_MixtureSameFamily`.
docstr_tok: ["Maybe","return","a","non-","`","CompositeTensor","`","`","_MixtureSameFamily","`","."]
code: def __new__(cls, *args, **kwargs):
      """Maybe return a non-`CompositeTensor` `_MixtureSameFamily`."""
  
      if cls is MixtureSameFamily:
          if args:
              mixture_distribution = args[0]
          else:
              mixture_distribution = kwargs.get("mixture_distribution")
          if len(args) > 1:
              components_distribution = args[1]
          else:
              components_distribution = kwargs.get(
                  "components_distribution"
              )
  
          if not (
              auto_composite_tensor.is_composite_tensor(
                  mixture_distribution
              )
              and auto_composite_tensor.is_composite_tensor(
                  components_distribution
              )
          ):
              return _MixtureSameFamily(*args, **kwargs)
      return super(MixtureSameFamily, cls).__new__(cls)
  
imports: import warnings
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import batch_broadcast
  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.internal import assert_util
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import custom_gradient
  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 samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math.gradient import value_and_batch_jacobian
label_desc: Use image node_id 1 for calling the MixtureSameFamily obj's underlying member method code with example usage: obj.__new__(cls) and returns: super, _MixtureSameFamily