code: def __init__(self, field, args):
      super().__init__()
      self.field = field
      self.args = args
      self.pad_idx = self.field.vocab.stoi[self.field.pad_token]
  
      def dp(args):
          return args.dropout_ratio if args.rnn_layers > 1 else 0.0
  
      if self.args.glove_and_char:
          self.encoder_embeddings = Embedding(
              field,
              args.dimension,
              dropout=args.dropout_ratio,
              project=not args.cove,
          )
  
          if self.args.cove or self.args.intermediate_cove:
              self.cove = MTLSTM(
                  model_cache=args.embeddings,
                  layer0=args.intermediate_cove,
                  layer1=args.cove,
              )
              cove_params = get_trainable_params(self.cove)
              for p in cove_params:
                  p.requires_grad = False
              cove_dim = (
                  int(args.intermediate_cove) * 600
                  + int(args.cove) * 600
                  + 400
              )  # the last 400 is for GloVe and char n-gram embeddings
              self.project_cove = Feedforward(cove_dim, args.dimension)
  
      if -1 not in self.args.elmo:
          options_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json"
          weight_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5"
          self.elmo = Elmo(
              options_file,
              weight_file,
              3,
              dropout=0.0,
              do_layer_norm=False,
          )
          elmo_params = get_trainable_params(self.elmo)
          for p in elmo_params:
              p.requires_grad = False
          elmo_dim = 1024 * len(self.args.elmo)
          self.project_elmo = Feedforward(elmo_dim, args.dimension)
          if self.args.glove_and_char:
              self.project_embeddings = Feedforward(
                  2 * args.dimension, args.dimension, dropout=0.0
              )
  
      self.decoder_embeddings = Embedding(
          field,
          args.dimension,
          dropout=args.dropout_ratio,
          project=True,
      )
  
      self.bilstm_before_coattention = PackedLSTM(
          args.dimension,
          args.dimension,
          batch_first=True,
          bidirectional=True,
          num_layers=1,
          dropout=0,
      )
      self.coattention = CoattentiveLayer(args.dimension, dropout=0.3)
      dim = 2 * args.dimension + args.dimension + args.dimension
  
      self.context_bilstm_after_coattention = PackedLSTM(
          dim,
          args.dimension,
          batch_first=True,
          dropout=dp(args),
          bidirectional=True,
          num_layers=args.rnn_layers,
      )
      self.self_attentive_encoder_context = TransformerEncoder(
          args.dimension,
          args.transformer_heads,
          args.transformer_hidden,
          args.transformer_layers,
          args.dropout_ratio,
      )
      self.bilstm_context = PackedLSTM(
          args.dimension,
          args.dimension,
          batch_first=True,
          dropout=dp(args),
          bidirectional=True,
          num_layers=args.rnn_layers,
      )
  
      self.question_bilstm_after_coattention = PackedLSTM(
          dim,
          args.dimension,
          batch_first=True,
          dropout=dp(args),
          bidirectional=True,
          num_layers=args.rnn_layers,
      )
      self.self_attentive_encoder_question = TransformerEncoder(
          args.dimension,
          args.transformer_heads,
          args.transformer_hidden,
          args.transformer_layers,
          args.dropout_ratio,
      )
      self.bilstm_question = PackedLSTM(
          args.dimension,
          args.dimension,
          batch_first=True,
          dropout=dp(args),
          bidirectional=True,
          num_layers=args.rnn_layers,
      )
  
      self.self_attentive_decoder = TransformerDecoder(
          args.dimension,
          args.transformer_heads,
          args.transformer_hidden,
          args.transformer_layers,
          args.dropout_ratio,
      )
      self.dual_ptr_rnn_decoder = DualPtrRNNDecoder(
          args.dimension,
          args.dimension,
          dropout=args.dropout_ratio,
          num_layers=args.rnn_layers,
      )
  
      self.generative_vocab_size = min(
          len(field.vocab), args.max_generative_vocab
      )
      self.out = nn.Linear(args.dimension, self.generative_vocab_size)
  
      self.dropout = nn.Dropout(0.4)
  
imports: import os
  import math
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from util import get_trainable_params
  from cove import MTLSTM
  from .common import positional_encodings_like, INF, EPSILON, TransformerEncoder, TransformerDecoder, PackedLSTM, LSTMDecoderAttention, LSTMDecoder, Embedding, Feedforward, mask, CoattentiveLayer
label_desc: Use image node_id 1 to create a new MultitaskQuestionAnsweringNetwork object from inherited base classes: nn with example: obj = MultitaskQuestionAnsweringNetwork(field, args)