class_docstr: Transformer model from `"Attention Is All You Need" (Vaswani et al, 2017)
  <https://arxiv.org/abs/1706.03762>`_, augmented with a pointer-generator
  network from `"Get To The Point: Summarization with Pointer-Generator
  Networks" (See et al, 2017) <https://arxiv.org/abs/1704.04368>`_.
  
  Args:
      encoder (TransformerPointerGeneratorEncoder): the encoder
      decoder (TransformerPointerGeneratorDecoder): the decoder
  
  The Transformer pointer-generator model provides the following named
  architectures and command-line arguments:
  
  .. argparse::
      :ref: fairseq.models.transformer_pointer_generator_parser
      :prog:
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code: def build_decoder(cls, args, tgt_dict, embed_tokens):
      return TransformerPointerGeneratorDecoder(
          args, tgt_dict, embed_tokens
      )
  
imports: import logging
  from typing import Any, Dict, Optional, List, Tuple
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.transformer import DEFAULT_MAX_SOURCE_POSITIONS, DEFAULT_MAX_TARGET_POSITIONS, TransformerDecoder, TransformerEncoder, TransformerModel, base_architecture
  from torch import Tensor
label_desc: Use image node_id 4 for calling the TransformerPointerGeneratorModel obj's underlying member method code with example usage: obj.build_decoder(cls, args, tgt_dict, embed_tokens) and returns: TransformerPointerGeneratorDecoder