code: def prepare_input_layer(
      input_layer_type: str,
      feats_dim: int,
      blocks: List[Dict[str, Any]],
      dropout_rate: float,
      pos_enc_dropout_rate: float,
  ) -> Dict[str, Any]:
      """Prepare input layer arguments.
  
      Args:
          input_layer_type: Input layer type.
          feats_dim: Dimension of input features.
          blocks: Blocks parameters for network part.
          dropout_rate: Dropout rate for input layer.
          pos_enc_dropout_rate: Dropout rate for input layer pos. enc.
  
      Return:
          input_block: Input block parameters.
  
      """
      input_block = {}
      first_block_type = blocks[0].get("type", None)
  
      if first_block_type == "causal-conv1d":
          input_block["type"] = "c-embed"
      else:
          input_block["type"] = input_layer_type
  
      input_block["dropout-rate"] = dropout_rate
      input_block["pos-dropout-rate"] = pos_enc_dropout_rate
  
      input_block["idim"] = feats_dim
  
      if first_block_type in ("transformer", "conformer"):
          input_block["odim"] = blocks[0].get("d_hidden", 0)
      else:
          input_block["odim"] = blocks[0].get("idim", 0)
  
      return input_block
  
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 2 for calling a global function with example usage: prepare_input_layer(input_layer_type, feats_dim, blocks, dropout_rate, pos_enc_dropout_rate) and returns: input_block