class_docstr: Transformer encoder module.
  
  Args:
      input_size: input dim
      output_size: dimension of attention
      attention_heads: the number of heads of multi head attention
      linear_units: the number of units of position-wise feed forward
      num_blocks: the number of decoder blocks
      dropout_rate: dropout rate
      attention_dropout_rate: dropout rate in attention
      positional_dropout_rate: dropout rate after adding positional encoding
      input_layer: input layer type
      pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
      normalize_before: whether to use layer_norm before the first block
      concat_after: whether to concat attention layer's input and output
          if True, additional linear will be applied.
          i.e. x -> x + linear(concat(x, att(x)))
          if False, no additional linear will be applied.
          i.e. x -> x + att(x)
      positionwise_layer_type: linear of conv1d
      positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
      padding_idx: padding_idx for input_layer=embed
class_docstr_tok: ["Transformer","encoder","module",".","Args",":","input_size",":","input","dim","output_size",":","dimension","of","attention","attention_heads",":","the","number","of","heads","of","multi","head","attention","linear_units",":","the","number","of","units","of","position-wise","feed","forward","num_blocks",":","the","number","of","decoder","blocks","dropout_rate",":","dropout","rate","attention_dropout_rate",":","dropout","rate","in","attention","positional_dropout_rate",":","dropout","rate","after","adding","positional","encoding","input_layer",":","input","layer","type","pos_enc_class",":","PositionalEncoding","or","ScaledPositionalEncoding","normalize_before",":","whether","to","use","layer_norm","before","the","first","block","concat_after",":","whether","to","concat","attention","layer","'s","input","and","output","if","True",",","additional","linear","will","be","applied",".","i.e",".","x","-",">","x","+","linear","(","concat","(","x",",","att","(","x",")",")",")","if","False",",","no","additional","linear","will","be","applied",".","i.e",".","x","-",">","x","+","att","(","x",")","positionwise_layer_type",":","linear","of","conv1d","positionwise_conv_kernel_size",":","kernel","size","of","positionwise","conv1d","layer","padding_idx",":","padding_idx","for","input_layer=embed"]
docstr: Embed positions in tensor.
  
  Args:
      xs_pad: input tensor (B, L, D)
      ilens: input length (B)
      prev_states: Not to be used now.
      ctc (CTC): ctc module for intermediate CTC loss
      return_all_hs (bool): whether to return all hidden states
  
  Returns:
      position embedded tensor and mask
docstr_tok: ["Embed","positions","in","tensor",".","Args",":","xs_pad",":","input","tensor","(","B",",","L",",","D",")","ilens",":","input","length","(","B",")","prev_states",":","Not","to","be","used","now",".","ctc","(","CTC",")",":","ctc","module","for","intermediate","CTC","loss","return_all_hs","(","bool",")",":","whether","to","return","all","hidden","states","Returns",":","position","embedded","tensor","and","mask"]
code: def forward(
      self,
      xs_pad: torch.Tensor,
      ilens: torch.Tensor,
      prev_states: torch.Tensor = None,
      ctc: CTC = None,
      return_all_hs: bool = False,
  ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
      """Embed positions in tensor.
  
      Args:
          xs_pad: input tensor (B, L, D)
          ilens: input length (B)
          prev_states: Not to be used now.
          ctc (CTC): ctc module for intermediate CTC loss
          return_all_hs (bool): whether to return all hidden states
  
      Returns:
          position embedded tensor and mask
      """
      masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
  
      if self.embed is None:
          xs_pad = xs_pad
      elif (
          isinstance(self.embed, Conv2dSubsampling)
          or isinstance(self.embed, Conv1dSubsampling2)
          or isinstance(self.embed, Conv2dSubsampling1)
          or isinstance(self.embed, Conv2dSubsampling2)
          or isinstance(self.embed, Conv2dSubsampling6)
          or isinstance(self.embed, Conv2dSubsampling8)
      ):
          short_status, limit_size = check_short_utt(
              self.embed, xs_pad.size(1)
          )
          if short_status:
              raise TooShortUttError(
                  f"has {xs_pad.size(1)} frames and is too short for subsampling "
                  + f"(it needs more than {limit_size} frames), return empty results",
                  xs_pad.size(1),
                  limit_size,
              )
          xs_pad, masks = self.embed(xs_pad, masks)
      else:
          xs_pad = self.embed(xs_pad)
  
      intermediate_outs = []
      if len(self.interctc_layer_idx) == 0:
          for layer_idx, encoder_layer in enumerate(self.encoders):
              xs_pad, masks = encoder_layer(xs_pad, masks)
              if return_all_hs:
                  if isinstance(xs_pad, tuple):
                      intermediate_outs.append(xs_pad[0])
                  else:
                      intermediate_outs.append(xs_pad)
      else:
          for layer_idx, encoder_layer in enumerate(self.encoders):
              xs_pad, masks = encoder_layer(xs_pad, masks)
  
              if layer_idx + 1 in self.interctc_layer_idx:
                  encoder_out = xs_pad
  
                  # intermediate outputs are also normalized
                  if self.normalize_before:
                      encoder_out = self.after_norm(encoder_out)
  
                  intermediate_outs.append((layer_idx + 1, encoder_out))
  
                  if self.interctc_use_conditioning:
                      ctc_out = ctc.softmax(encoder_out)
                      xs_pad = xs_pad + self.conditioning_layer(ctc_out)
  
      if self.normalize_before:
          xs_pad = self.after_norm(xs_pad)
  
      olens = masks.squeeze(1).sum(1)
      if len(intermediate_outs) > 0:
          return (xs_pad, intermediate_outs), olens, None
      return xs_pad, olens, None
  
file_path: espnet/espnet2/asr/encoder/transformer_encoder.py
imports: from typing import List, Optional, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.ctc import CTC
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear, MultiLayeredConv1d
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import repeat
  from espnet.nets.pytorch_backend.transformer.subsampling import Conv1dSubsampling2, Conv2dSubsampling, Conv2dSubsampling1, Conv2dSubsampling2, Conv2dSubsampling6, Conv2dSubsampling8, TooShortUttError, check_short_utt
label_desc: Use image node_id 3 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.forward(xs_pad, ilens, prev_states, ctc, return_all_hs) and returns: xs_pad,  olens,  None, xs_pad,  intermediate_outs,  olens,  None