code: def build_input_layer(
      block: Dict[str, Any],
      pos_enc_class: torch.nn.Module,
      padding_idx: int,
  ) -> Tuple[Union[Conv2dSubsampling, VGG2L, torch.nn.Sequential], int]:
      """Build input layer.
  
      Args:
          block: Architecture definition of input layer.
          pos_enc_class: Positional encoding class.
          padding_idx: Padding symbol ID for embedding layer (if provided).
  
      Returns:
          : Input layer module.
          subsampling_factor: Subsampling factor.
  
      """
      input_type = block["type"]
  
      idim = block["idim"]
      odim = block["odim"]
  
      dropout_rate = block["dropout-rate"]
      pos_dropout_rate = block["pos-dropout-rate"]
  
      if pos_enc_class.__name__ == "RelPositionalEncoding":
          pos_enc_class_subsampling = pos_enc_class(
              odim, pos_dropout_rate
          )
      else:
          pos_enc_class_subsampling = None
  
      if input_type == "linear":
          return (
              torch.nn.Sequential(
                  torch.nn.Linear(idim, odim),
                  torch.nn.LayerNorm(odim),
                  torch.nn.Dropout(dropout_rate),
                  torch.nn.ReLU(),
                  pos_enc_class(odim, pos_dropout_rate),
              ),
              1,
          )
      elif input_type == "conv2d":
          return (
              Conv2dSubsampling(
                  idim, odim, dropout_rate, pos_enc_class_subsampling
              ),
              4,
          )
      elif input_type == "vgg2l":
          return VGG2L(idim, odim, pos_enc_class_subsampling), 4
      elif input_type == "embed":
          return (
              torch.nn.Sequential(
                  torch.nn.Embedding(
                      idim, odim, padding_idx=padding_idx
                  ),
                  pos_enc_class(odim, pos_dropout_rate),
              ),
              1,
          )
      elif input_type == "c-embed":
          return (
              torch.nn.Sequential(
                  torch.nn.Embedding(
                      idim, odim, padding_idx=padding_idx
                  ),
                  torch.nn.Dropout(dropout_rate),
              ),
              1,
          )
      else:
          raise NotImplementedError(
              "Invalid input layer: %s. Supported: linear, conv2d, vgg2l and embed"
              % input_type
          )
  
imports: from typing import Any, Dict, List, Tuple, Union
  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.conv1d_nets import CausalConv1d, Conv1d
  from espnet.nets.pytorch_backend.transducer.transformer_decoder_layer import TransformerDecoderLayer
  from espnet.nets.pytorch_backend.transducer.vgg2l import VGG2L
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention, RelPositionMultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding, RelPositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import MultiSequential
  from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling
label_desc: Use image node_id 5 for calling a global function with example usage: build_input_layer(block, pos_enc_class, padding_idx) and returns: torch,  int, Conv2dSubsampling,  int, VGG2L,  int, torch,  int, torch,  int