class_docstr: Conformer encoder module.
  
  Args:
      idim (int): Input dimension.
      attention_dim (int): Dimension of attention.
      attention_heads (int): The number of heads of multi head attention.
      linear_units (int): The number of units of position-wise feed forward.
      num_blocks (int): The number of decoder blocks.
      dropout_rate (float): Dropout rate.
      positional_dropout_rate (float): Dropout rate after adding positional encoding.
      attention_dropout_rate (float): Dropout rate in attention.
      input_layer (Union[str, torch.nn.Module]): Input layer type.
      normalize_before (bool): Whether to use layer_norm before the first block.
      concat_after (bool): 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 (str): "linear", "conv1d", or "conv1d-linear".
      positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
      macaron_style (bool): Whether to use macaron style for positionwise layer.
      pos_enc_layer_type (str): Encoder positional encoding layer type.
      selfattention_layer_type (str): Encoder attention layer type.
      activation_type (str): Encoder activation function type.
      use_cnn_module (bool): Whether to use convolution module.
      zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
      cnn_module_kernel (int): Kernerl size of convolution module.
      padding_idx (int): Padding idx for input_layer=embed.
      stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
      intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
          indices start from 1.
          if not None, intermediate outputs are returned (which changes return type
          signature.)
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docstr: Construct an Encoder object.
docstr_tok: ["Construct","an","Encoder","object","."]
code: def __init__(
      self,
      idim,
      attention_dim=256,
      attention_heads=4,
      linear_units=2048,
      num_blocks=6,
      dropout_rate=0.1,
      positional_dropout_rate=0.1,
      attention_dropout_rate=0.0,
      input_layer="conv2d",
      normalize_before=True,
      concat_after=False,
      positionwise_layer_type="linear",
      positionwise_conv_kernel_size=1,
      macaron_style=False,
      pos_enc_layer_type="abs_pos",
      selfattention_layer_type="selfattn",
      activation_type="swish",
      use_cnn_module=False,
      zero_triu=False,
      cnn_module_kernel=31,
      padding_idx=-1,
      stochastic_depth_rate=0.0,
      intermediate_layers=None,
      ctc_softmax=None,
      conditioning_layer_dim=None,
  ):
      """Construct an Encoder object."""
      super(Encoder, self).__init__()
  
      activation = get_activation(activation_type)
      if pos_enc_layer_type == "abs_pos":
          pos_enc_class = PositionalEncoding
      elif pos_enc_layer_type == "scaled_abs_pos":
          pos_enc_class = ScaledPositionalEncoding
      elif pos_enc_layer_type == "rel_pos":
          assert selfattention_layer_type == "rel_selfattn"
          pos_enc_class = RelPositionalEncoding
      elif pos_enc_layer_type == "legacy_rel_pos":
          pos_enc_class = LegacyRelPositionalEncoding
          assert selfattention_layer_type == "legacy_rel_selfattn"
      else:
          raise ValueError(
              "unknown pos_enc_layer: " + pos_enc_layer_type
          )
  
      self.conv_subsampling_factor = 1
      if input_layer == "linear":
          self.embed = torch.nn.Sequential(
              torch.nn.Linear(idim, attention_dim),
              torch.nn.LayerNorm(attention_dim),
              torch.nn.Dropout(dropout_rate),
              pos_enc_class(attention_dim, positional_dropout_rate),
          )
      elif input_layer == "conv2d":
          self.embed = Conv2dSubsampling(
              idim,
              attention_dim,
              dropout_rate,
              pos_enc_class(attention_dim, positional_dropout_rate),
          )
          self.conv_subsampling_factor = 4
      elif input_layer == "vgg2l":
          self.embed = VGG2L(idim, attention_dim)
          self.conv_subsampling_factor = 4
      elif input_layer == "embed":
          self.embed = torch.nn.Sequential(
              torch.nn.Embedding(
                  idim, attention_dim, padding_idx=padding_idx
              ),
              pos_enc_class(attention_dim, positional_dropout_rate),
          )
      elif isinstance(input_layer, torch.nn.Module):
          self.embed = torch.nn.Sequential(
              input_layer,
              pos_enc_class(attention_dim, positional_dropout_rate),
          )
      elif input_layer is None:
          self.embed = torch.nn.Sequential(
              pos_enc_class(attention_dim, positional_dropout_rate)
          )
      else:
          raise ValueError("unknown input_layer: " + input_layer)
      self.normalize_before = normalize_before
  
      # self-attention module definition
      if selfattention_layer_type == "selfattn":
          logging.info(
              "encoder self-attention layer type = self-attention"
          )
          encoder_selfattn_layer = MultiHeadedAttention
          encoder_selfattn_layer_args = (
              attention_heads,
              attention_dim,
              attention_dropout_rate,
          )
      elif selfattention_layer_type == "legacy_rel_selfattn":
          assert pos_enc_layer_type == "legacy_rel_pos"
          encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
          encoder_selfattn_layer_args = (
              attention_heads,
              attention_dim,
              attention_dropout_rate,
          )
      elif selfattention_layer_type == "rel_selfattn":
          logging.info(
              "encoder self-attention layer type = relative self-attention"
          )
          assert pos_enc_layer_type == "rel_pos"
          encoder_selfattn_layer = RelPositionMultiHeadedAttention
          encoder_selfattn_layer_args = (
              attention_heads,
              attention_dim,
              attention_dropout_rate,
              zero_triu,
          )
      else:
          raise ValueError(
              "unknown encoder_attn_layer: " + selfattention_layer_type
          )
  
      # feed-forward module definition
      if positionwise_layer_type == "linear":
          positionwise_layer = PositionwiseFeedForward
          positionwise_layer_args = (
              attention_dim,
              linear_units,
              dropout_rate,
              activation,
          )
      elif positionwise_layer_type == "conv1d":
          positionwise_layer = MultiLayeredConv1d
          positionwise_layer_args = (
              attention_dim,
              linear_units,
              positionwise_conv_kernel_size,
              dropout_rate,
          )
      elif positionwise_layer_type == "conv1d-linear":
          positionwise_layer = Conv1dLinear
          positionwise_layer_args = (
              attention_dim,
              linear_units,
              positionwise_conv_kernel_size,
              dropout_rate,
          )
      else:
          raise NotImplementedError("Support only linear or conv1d.")
  
      # convolution module definition
      convolution_layer = ConvolutionModule
      convolution_layer_args = (
          attention_dim,
          cnn_module_kernel,
          activation,
      )
  
      self.encoders = repeat(
          num_blocks,
          lambda lnum: EncoderLayer(
              attention_dim,
              encoder_selfattn_layer(*encoder_selfattn_layer_args),
              positionwise_layer(*positionwise_layer_args),
              positionwise_layer(*positionwise_layer_args)
              if macaron_style
              else None,
              convolution_layer(*convolution_layer_args)
              if use_cnn_module
              else None,
              dropout_rate,
              normalize_before,
              concat_after,
              stochastic_depth_rate * float(1 + lnum) / num_blocks,
          ),
      )
      if self.normalize_before:
          self.after_norm = LayerNorm(attention_dim)
  
      self.intermediate_layers = intermediate_layers
      self.use_conditioning = True if ctc_softmax is not None else False
      if self.use_conditioning:
          self.ctc_softmax = ctc_softmax
          self.conditioning_layer = torch.nn.Linear(
              conditioning_layer_dim, attention_dim
          )
  
file_path: espnet/espnet/nets/pytorch_backend/conformer/encoder.py
imports: import logging
  import torch
  from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
  from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.nets_utils import get_activation
  from espnet.nets.pytorch_backend.transducer.vgg2l import VGG2L
  from espnet.nets.pytorch_backend.transformer.attention import LegacyRelPositionMultiHeadedAttention, MultiHeadedAttention, RelPositionMultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.embedding import LegacyRelPositionalEncoding, PositionalEncoding, RelPositionalEncoding, ScaledPositionalEncoding
  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 Conv2dSubsampling
label_desc: Use image node_id 1 to create a new Encoder object from inherited base classes: torch.nn with example: obj = Encoder(idim, attention_dim, attention_heads, linear_units, num_blocks, dropout_rate, positional_dropout_rate, attention_dropout_rate, input_layer, normalize_before, concat_after, positionwise_layer_type, positionwise_conv_kernel_size, macaron_style, pos_enc_layer_type, attention_layer_type, activation_type, use_cnn_module, zero_triu, cnn_module_kernel, padding_idx, stochastic_depth_rate, intermediate_layers, ctc_softmax, conditioning_layer_dim)