def __init__(self,<EOL>event_size,<EOL>num_components,<EOL>convert_to_tensor_fn=tfd.Distribution.sample,<EOL>sample_dtype=None,<EOL>validate_args=False,<EOL>**kwargs):
super(CategoricalMixtureOfOneHotCategorical, self).__init__(<EOL>lambda t: CategoricalMixtureOfOneHotCategorical.new(<EOL>t, event_size, num_components, sample_dtype, validate_args),<EOL>convert_to_tensor_fn,<EOL>**kwargs)<EOL>
Initialize the `CategoricalMixtureOfOneHotCategorical` layer.

        Args:
          event_size: Scalar `int` representing the size of single draw from this
            distribution.
          num_components: Scalar `int` representing the number of mixture
            components. Must be at least 1. (If `num_components=1`, it's more
            efficient to use the `OneHotCategorical` layer.)
          convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
            instance and returns a `tf.Tensor`-like object. For examples, see
            `class` docstring.
            Default value: `tfd.Distribution.sample`.
          sample_dtype: `dtype` of samples produced by this distribution.
            Default value: `None` (i.e., previous layer's `dtype`).
          validate_args: Python `bool`, default `False`. When `True` distribution
            parameters are checked for validity despite possibly degrading runtime
            performance. When `False` invalid inputs may silently render incorrect
            outputs.
            Default value: `False`.
          **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.