class_docstr: ArcII Model.
  
  Examples:
      >>> model = ArcII()
      >>> model.params['embedding_output_dim'] = 300
      >>> model.params['num_blocks'] = 2
      >>> model.params['kernel_1d_count'] = 32
      >>> model.params['kernel_1d_size'] = 3
      >>> model.params['kernel_2d_count'] = [16, 32]
      >>> model.params['kernel_2d_size'] = [[3, 3], [3, 3]]
      >>> model.params['pool_2d_size'] = [[2, 2], [2, 2]]
      >>> model.guess_and_fill_missing_params(verbose=0)
      >>> model.build()
docstr: :return: model default parameters.
code: def get_default_params(cls) -> ParamTable:
      """:return: model default parameters."""
      params = super().get_default_params(with_embedding=True)
      params["optimizer"] = "adam"
      opt_space = hyper_spaces.choice(["adam", "rmsprop", "adagrad"])
      params.get("optimizer").hyper_space = opt_space
      params.add(
          Param(
              name="num_blocks",
              value=1,
              desc="Number of 2D convolution blocks.",
          )
      )
      params.add(
          Param(
              name="kernel_1d_count",
              value=32,
              desc="Kernel count of 1D convolution layer.",
          )
      )
      params.add(
          Param(
              name="kernel_1d_size",
              value=3,
              desc="Kernel size of 1D convolution layer.",
          )
      )
      params.add(
          Param(
              name="kernel_2d_count",
              value=[32],
              desc="Kernel count of 2D convolution layer in"
              "each block",
          )
      )
      params.add(
          Param(
              name="kernel_2d_size",
              value=[[3, 3]],
              desc="Kernel size of 2D convolution layer in"
              " each block.",
          )
      )
      params.add(
          Param(
              name="activation",
              value="relu",
              desc="Activation function.",
          )
      )
      params.add(
          Param(
              name="pool_2d_size",
              value=[[2, 2]],
              desc="Size of pooling layer in each block.",
          )
      )
      params.add(
          Param(
              name="padding",
              value="same",
              hyper_space=hyper_spaces.choice(["same", "valid"]),
              desc="The padding mode in the convolution layer. It should be one"
              "of `same`, `valid`.",
          )
      )
      params.add(
          Param(
              name="dropout_rate",
              value=0.0,
              hyper_space=hyper_spaces.quniform(
                  low=0.0, high=0.8, q=0.01
              ),
              desc="The dropout rate.",
          )
      )
      return params
  
file_path: MatchZoo/matchzoo/models/arcii.py
imports: import typing
  import keras
  import matchzoo
  from matchzoo.engine.base_model import BaseModel
  from matchzoo.engine.param import Param
  from matchzoo.engine.param_table import ParamTable
  from matchzoo.engine import hyper_spaces
label_desc: Use image node_id 1 for calling the ArcII obj's underlying member method code with example usage: obj.get_default_params(cls) and returns: params