class_docstr: A simple densely connected baseline model.
  
  Examples:
      >>> model = DenseBaseline()
      >>> model.params['mlp_num_layers'] = 2
      >>> model.params['mlp_num_units'] = 300
      >>> model.params['mlp_num_fan_out'] = 128
      >>> model.params['mlp_activation_func'] = 'relu'
      >>> model.guess_and_fill_missing_params(verbose=0)
      >>> model.build()
      >>> model.compile()
docstr: :return: model default parameters.
docstr_tok: [":","return",":","model","default","parameters","."]
code: def get_default_params(cls) -> ParamTable:
      """:return: model default parameters."""
      params = super().get_default_params(
          with_multi_layer_perceptron=True
      )
      params["mlp_num_units"] = 256
      params.get("mlp_num_units").hyper_space = hyper_spaces.quniform(
          16, 512
      )
      params.get("mlp_num_layers").hyper_space = hyper_spaces.quniform(
          1, 5
      )
      return params
  
imports: import keras.layers
  from matchzoo.engine.base_model import BaseModel
  from matchzoo.engine.param_table import ParamTable
  from matchzoo.engine import hyper_spaces
label_desc: Use image node_id 1 for calling the DenseBaseline obj's underlying member method code with example usage: obj.get_default_params(cls) and returns: params