class_docstr: Simple CategoricalGRUModel for testing.
  
  Args:
      output_dim (int): Dimension of the network output.
      hidden_dim (int): Hidden dimension for GRU cell.
      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","CategoricalGRUModel","for","testing",".","Args",":","output_dim","(","int",")",":","Dimension","of","the","network","output",".","hidden_dim","(","int",")",":","Hidden","dimension","for","GRU","cell",".","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, hidden_dim, name, *args, **kwargs):
      super().__init__(output_dim, hidden_dim, name)
  
imports: import tensorflow_probability
  from tests.fixtures.models.simple_gru_model import SimpleGRUModel
label_desc: Use image node_id 1 to create a new SimpleCategoricalGRUModel object from inherited base classes: SimpleGRUModel with example: obj = SimpleCategoricalGRUModel(output_dim, hidden_dim, name)