class_docstr: ConvKNRM model.
  
  Examples:
      >>> model = ConvKNRM()
      >>> model.params['embedding_input_dim'] = 10000
      >>> model.params['embedding_output_dim'] = 300
      >>> model.params['embedding_trainable'] = True
      >>> model.params['filters'] = 128
      >>> model.params['conv_activation_func'] = 'tanh'
      >>> model.params['max_ngram'] = 3
      >>> model.params['use_crossmatch'] = True
      >>> model.params['kernel_num'] = 11
      >>> model.params['sigma'] = 0.1
      >>> model.params['exact_sigma'] = 0.001
      >>> model.guess_and_fill_missing_params(verbose=0)
      >>> model.build()
docstr: Get default parameters.
docstr_tok: ["Get","default","parameters","."]
code: def get_default_params(cls):
      """Get default parameters."""
      params = super().get_default_params()
      params.add(
          Param(
              name="filters",
              value=128,
              desc="The filter size in the convolution" " layer.",
          )
      )
      params.add(
          Param(
              name="conv_activation_func",
              value="relu",
              desc="The activation function in the "
              "convolution layer.",
          )
      )
      params.add(
          Param(
              name="max_ngram",
              value=3,
              desc="The maximum length of n-grams for the "
              "convolution layer.",
          )
      )
      params.add(
          Param(
              name="use_crossmatch",
              value=True,
              desc="Whether to match left n-grams and right "
              "n-grams of different lengths",
          )
      )
      return params
  
imports: import keras
  import tensorflow
  from .knrm import KNRM
  from matchzoo.engine.param import Param
label_desc: Use image node_id 1 for calling the ConvKNRM obj's underlying member method code with example usage: obj.get_default_params(cls) and returns: params