class_docstr: Example agent.
  
  Implements the interface for TorchGeneratorAgent. The minimum requirement is that it
  implements ``build_model``, but we will want to include additional command line
  parameters.
class_docstr_tok: ["Example","agent",".","Implements","the","interface","for","TorchGeneratorAgent",".","The","minimum","requirement","is","that","it","implements","``","build_model","``",",","but","we","will","want","to","include","additional","command","line","parameters","."]
docstr: Construct the model.
docstr_tok: ["Construct","the","model","."]
code: def build_model(self):
      """
      Construct the model.
      """
  
      model = ExampleModel(self.dict, self.opt["hidden_size"])
      # Optionally initialize pre-trained embeddings by copying them from another
      # source: GloVe, fastText, etc.
      self._copy_embeddings(
          model.embeddings.weight, self.opt["embedding_type"]
      )
      return model
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch.nn
  import torch.nn.functional
  import parlai.core.torch_generator_agent
label_desc: Use image node_id 2 for calling the Seq2seqAgent obj's underlying member method code with example usage: obj.build_model() and returns: model