class_docstr: Mixture of distributions with additive i.i.d. uniform noise.
class_docstr_tok: ["Mixture","of","distributions","with","additive","i.i.d",".","uniform","noise","."]
docstr: Initializer, taking the same arguments as `tfpd.MixtureSameFamily`.
docstr_tok: ["Initializer",",","taking","the","same","arguments","as","`","tfpd.MixtureSameFamily","`","."]
code: def __init__(
      self,
      mixture_distribution,
      components_distribution,
      name="NoisyMixtureSameFamily",
  ):
      """Initializer, taking the same arguments as `tfpd.MixtureSameFamily`."""
      super().__init__(
          mixture_distribution=mixture_distribution,
          components_distribution=UniformNoiseAdapter(
              components_distribution
          ),
          name=name,
      )
      self._base = tfp.distributions.MixtureSameFamily(
          mixture_distribution=mixture_distribution,
          components_distribution=components_distribution,
          name=name + "Base",
      )
  
imports: import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import helpers
label_desc: Use image node_id 1 to create a new NoisyMixtureSameFamily object from inherited base classes: tfp.distributions with example: obj = NoisyMixtureSameFamily(mixture_distribution, components_distribution, name)