class_docstr: Simple CategoricalMLPModel for testing.
  
  Args:
      output_dim (int): Dimension of the network output.
      name (str): Policy name, also the variable scope.
      args: Extra arguments which are not used.
      kwargs: Extra keyword arguments which are not used.
class_docstr_tok: ["Simple","CategoricalMLPModel","for","testing",".","Args",":","output_dim","(","int",")",":","Dimension","of","the","network","output",".","name","(","str",")",":","Policy","name",",","also","the","variable","scope",".","args",":","Extra","arguments","which","are","not","used",".","kwargs",":","Extra","keyword","arguments","which","are","not","used","."]
code: def __init__(self, output_dim, name, *args, **kwargs):
      super().__init__(output_dim, name)
  
imports: import tensorflow_probability
  from tests.fixtures.models.simple_mlp_model import SimpleMLPModel
label_desc: Use image node_id 1 to create a new SimpleCategoricalMLPModel object from inherited base classes: SimpleMLPModel with example: obj = SimpleCategoricalMLPModel(output_dim, name)