class_docstr: Categorical MLP Model.
  
  A model represented by a Categorical distribution
  which is parameterized by a multilayer perceptron (MLP).
  
  Args:
      output_dim (int): Dimension of the network output.
      hidden_sizes (list[int]): Output dimension of dense layer(s).
          For example, (32, 32) means this MLP consists of two
          hidden layers, each with 32 hidden units.
      name (str): Model name, also the variable scope.
      hidden_nonlinearity (callable): Activation function for intermediate
          dense layer(s). It should return a tf.Tensor. Set it to
          None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      output_nonlinearity (callable): Activation function for output dense
          layer. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          tf.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          tf.Tensor.
      layer_normalization (bool): Bool for using layer normalization or not.
code: def __init__(
      self,
      output_dim,
      name=None,
      hidden_sizes=(32, 32),
      hidden_nonlinearity=tf.nn.tanh,
      hidden_w_init=tf.initializers.glorot_uniform(
          seed=deterministic.get_tf_seed_stream()
      ),
      hidden_b_init=tf.zeros_initializer(),
      output_nonlinearity=tf.nn.softmax,
      output_w_init=tf.initializers.glorot_uniform(
          seed=deterministic.get_tf_seed_stream()
      ),
      output_b_init=tf.zeros_initializer(),
      layer_normalization=False,
  ):
      super().__init__(
          output_dim,
          name,
          hidden_sizes,
          hidden_nonlinearity,
          hidden_w_init,
          hidden_b_init,
          output_nonlinearity,
          output_w_init,
          output_b_init,
          layer_normalization,
      )
  
file_path: garage/src/garage/tf/models/categorical_mlp_model.py
imports: import tensorflow
  import tensorflow_probability
  from garage.experiment import deterministic
  from garage.tf.models.mlp_model import MLPModel
label_desc: Use image node_id 1 to create a new CategoricalMLPModel object from inherited base classes: MLPModel with example: obj = CategoricalMLPModel(output_dim, name, hidden_sizes, hidden_nonlinearity, hidden_w_init, hidden_b_init, output_nonlinearity, output_w_init, output_b_init, layer_normalization)