code: def build_conformer_block(
      block: Dict[str, Any],
      self_attn_class: str,
      pw_layer_type: str,
      pw_activation_type: str,
      conv_mod_activation_type: str,
  ) -> ConformerEncoderLayer:
      """Build function for conformer block.
  
      Args:
          block: Conformer block parameters.
          self_attn_type: Self-attention module type.
          pw_layer_type: Positionwise layer type.
          pw_activation_type: Positionwise activation type.
          conv_mod_activation_type: Convolutional module activation type.
  
      Returns:
          : Function to create conformer (encoder) block.
  
      """
      d_hidden = block["d_hidden"]
      d_ff = block["d_ff"]
  
      dropout_rate = block.get("dropout-rate", 0.0)
      pos_dropout_rate = block.get("pos-dropout-rate", 0.0)
      att_dropout_rate = block.get("att-dropout-rate", 0.0)
  
      macaron_style = block["macaron_style"]
      use_conv_mod = block["use_conv_mod"]
  
      if pw_layer_type == "linear":
          pw_layer = PositionwiseFeedForward
          pw_layer_args = (
              d_hidden,
              d_ff,
              pos_dropout_rate,
              get_activation(pw_activation_type),
          )
      else:
          raise NotImplementedError(
              "Conformer block only supports linear yet."
          )
  
      if macaron_style:
          macaron_net = PositionwiseFeedForward
          macaron_net_args = (
              d_hidden,
              d_ff,
              pos_dropout_rate,
              get_activation(pw_activation_type),
          )
  
      if use_conv_mod:
          conv_mod = ConvolutionModule
          conv_mod_args = (
              d_hidden,
              block["conv_mod_kernel"],
              get_activation(conv_mod_activation_type),
          )
  
      return lambda: ConformerEncoderLayer(
          d_hidden,
          self_attn_class(block["heads"], d_hidden, att_dropout_rate),
          pw_layer(*pw_layer_args),
          macaron_net(*macaron_net_args) if macaron_style else None,
          conv_mod(*conv_mod_args) if use_conv_mod else None,
          dropout_rate,
      )
  
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 7 for calling a global function with example usage: build_conformer_block(block, _attn_class, pw_layer_type, pw_activation_type, conv_mod_activation_type) and returns: function