class_docstr: Load a pre-trained embedding after the model is built.
  
  Used with tuner to load a pre-trained embedding matrix for each newly built
  model instance.
  
  :param embedding_matrix: Embedding matrix to load.
  
  Example:
  
      >>> import matchzoo as mz
      >>> model = mz.models.ArcI()
      >>> prpr = model.get_default_preprocessor()
      >>> data = mz.datasets.toy.load_data()
      >>> data = prpr.fit_transform(data, verbose=0)
      >>> embed = mz.datasets.toy.load_embedding()
      >>> term_index = prpr.context['vocab_unit'].state['term_index']
      >>> matrix = embed.build_matrix(term_index)
      >>> callback = mz.auto.tuner.callbacks.LoadEmbeddingMatrix(matrix)
      >>> model.params.update(prpr.context)
      >>> model.params['task'] = mz.tasks.Ranking()
      >>> model.params['embedding_output_dim'] = embed.output_dim
      >>> result = mz.auto.tune(
      ...     params=model.params,
      ...     train_data=data,
      ...     test_data=data,
      ...     num_runs=1,
      ...     callbacks=[callback],
      ...     verbose=0
      ... )
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docstr: `on_build_end`.
docstr_tok: ["`","on_build_end","`","."]
code: def on_build_end(self, tuner, model: BaseModel):
      """`on_build_end`."""
      model.load_embedding_matrix(self._embedding_matrix)
  
imports: from matchzoo.engine.base_model import BaseModel
  from matchzoo.auto.tuner.callbacks.callback import Callback
label_desc: Use image node_id 2 for calling the LoadEmbeddingMatrix obj's underlying member method code with example usage: obj.on_build_end(tuner, model) without return types