class_docstr: E2E module.
  
  :param int idim: dimension of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
docstr: Construct an E2E object.
  
  :param int idim: dimension of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
docstr_tok: ["Construct","an","E2E","object",".",":","param","int","idim",":","dimension","of","inputs",":","param","int","odim",":","dimension","of","outputs",":","param","Namespace","args",":","argument","Namespace","containing","options"]
code: def __init__(self, idim, odim, args, ignore_id=-1):
      """Construct an E2E object.
  
      :param int idim: dimension of inputs
      :param int odim: dimension of outputs
      :param Namespace args: argument Namespace containing options
      """
      super().__init__(idim, odim, args, ignore_id)
      if args.transformer_attn_dropout_rate is None:
          args.transformer_attn_dropout_rate = args.dropout_rate
  
      # Check the relative positional encoding type
      args = verify_rel_pos_type(args)
  
      self.encoder = Encoder(
          idim=idim,
          attention_dim=args.adim,
          attention_heads=args.aheads,
          linear_units=args.eunits,
          num_blocks=args.elayers,
          input_layer=args.transformer_input_layer,
          dropout_rate=args.dropout_rate,
          positional_dropout_rate=args.dropout_rate,
          attention_dropout_rate=args.transformer_attn_dropout_rate,
          pos_enc_layer_type=args.transformer_encoder_pos_enc_layer_type,
          selfattention_layer_type=args.transformer_encoder_selfattn_layer_type,
          activation_type=args.transformer_encoder_activation_type,
          macaron_style=args.macaron_style,
          use_cnn_module=args.use_cnn_module,
          cnn_module_kernel=args.cnn_module_kernel,
      )
      self.reset_parameters(args)
  
imports: from espnet.nets.pytorch_backend.conformer.argument import add_arguments_conformer_common, verify_rel_pos_type
  from espnet.nets.pytorch_backend.conformer.encoder import Encoder
  from espnet.nets.pytorch_backend.e2e_st_transformer import E2E
label_desc: Use image node_id 3 to create a new E2E object from inherited base classes: E2ETransformer with example: obj = E2E(idim, odim, args, ignore_id)