class_docstr: DUET Model.
  
  Examples:
      >>> model = DUET()
      >>> model.params['embedding_input_dim'] = 1000
      >>> model.params['embedding_output_dim'] = 300
      >>> model.params['lm_filters'] = 32
      >>> model.params['lm_hidden_sizes'] = [64, 32]
      >>> model.params['dropout_rate'] = 0.5
      >>> model.params['dm_filters'] = 32
      >>> model.params['dm_kernel_size'] = 3
      >>> model.params['dm_d_mpool'] = 4
      >>> model.params['dm_hidden_sizes'] = [64, 32]
      >>> model.guess_and_fill_missing_params(verbose=0)
      >>> model.build()
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docstr: Get default parameters.
docstr_tok: ["Get","default","parameters","."]
code: def get_default_params(cls):
      """Get default parameters."""
      params = super().get_default_params(with_embedding=True)
      params.add(
          Param(
              name="lm_filters",
              value=32,
              desc="Filter size of 1D convolution layer in "
              "the local model.",
          )
      )
      params.add(
          Param(
              name="lm_hidden_sizes",
              value=[32],
              desc="A list of hidden size of the MLP layer "
              "in the local model.",
          )
      )
      params.add(
          Param(
              name="dm_filters",
              value=32,
              desc="Filter size of 1D convolution layer in "
              "the distributed model.",
          )
      )
      params.add(
          Param(
              name="dm_kernel_size",
              value=3,
              desc="Kernel size of 1D convolution layer in "
              "the distributed model.",
          )
      )
      params.add(
          Param(
              name="dm_q_hidden_size",
              value=32,
              desc="Hidden size of the MLP layer for the "
              "left text in the distributed model.",
          )
      )
      params.add(
          Param(
              name="dm_d_mpool",
              value=3,
              desc="Max pooling size for the right text in "
              "the distributed model.",
          )
      )
      params.add(
          Param(
              name="dm_hidden_sizes",
              value=[32],
              desc="A list of hidden size of the MLP layer "
              "in the distributed model.",
          )
      )
      params.add(
          Param(
              name="padding",
              value="same",
              desc="The padding mode in the convolution "
              "layer. It should be one of `same`, "
              "`valid`, "
              "and `causal`.",
          )
      )
      params.add(
          Param(
              name="activation_func",
              value="relu",
              desc="Activation function in the convolution" " layer.",
          )
      )
      params.add(
          Param(
              name="dropout_rate",
              value=0.5,
              hyper_space=hyper_spaces.quniform(
                  low=0.0, high=0.8, q=0.02
              ),
              desc="The dropout rate.",
          )
      )
      return params
  
file_path: MatchZoo/matchzoo/models/duet.py
imports: import keras
  import tensorflow
  from matchzoo.engine import hyper_spaces
  from matchzoo.engine.base_model import BaseModel
  from matchzoo.engine.param import Param
label_desc: Use image node_id 1 for calling the DUET obj's underlying member method code with example usage: obj.get_default_params(cls) and returns: params