class_docstr: Mixture of normal distributions with additive i.i.d. uniform noise.
class_docstr_tok: ["Mixture","of","normal","distributions","with","additive","i.i.d",".","uniform","noise","."]
docstr: Initializer.
  
  Args:
    loc: Location parameters of `tfpd.Normal` component distributions.
    scale: Scale parameters of `tfpd.Normal` component distributions.
    weight: `probs` parameter of `tfpd.Categorical` mixture distribution.
    name: A name for this distribution.
docstr_tok: ["Initializer",".","Args",":","loc",":","Location","parameters","of","`","tfpd.Normal","`","component","distributions",".","scale",":","Scale","parameters","of","`","tfpd.Normal","`","component","distributions",".","weight",":","`","probs","`","parameter","of","`","tfpd.Categorical","`","mixture","distribution",".","name",":","A","name","for","this","distribution","."]
code: def __init__(self, loc, scale, weight, name="NoisyNormalMixture"):
      """Initializer.
  
      Args:
        loc: Location parameters of `tfpd.Normal` component distributions.
        scale: Scale parameters of `tfpd.Normal` component distributions.
        weight: `probs` parameter of `tfpd.Categorical` mixture distribution.
        name: A name for this distribution.
      """
      super().__init__(
          mixture_distribution=tfp.distributions.Categorical(
              probs=weight
          ),
          components_distribution=tfp.distributions.Normal(
              loc=loc, scale=scale
          ),
          name=name,
      )
  
imports: import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import helpers
label_desc: Use image node_id 1 to create a new NoisyNormalMixture object from inherited base classes: NoisyMixtureSameFamily with example: obj = NoisyNormalMixture(loc, scale, weight, name)