code: def get_configuration(opts: argparse.Namespace) -> Dict:
      """
      Get configuration parameters associated with ByteFormer.
  
      These parameters are similar to those of DeIT
      (https://arxiv.org/pdf/2012.12877.pdf).
  
      Args:
          opts: The options configuration.
  
      Returns:
          A dict with keys specifying the parameters needed for ByteFormer.
      """
      mode = getattr(opts, "model.classification.byteformer.mode")
      mode = mode.lower()
      dropout = getattr(opts, "model.classification.byteformer.dropout")
      norm_layer = getattr(
          opts, "model.classification.byteformer.norm_layer"
      )
  
      byteformer_config = dict()
      if mode == "tiny":
          byteformer_config = {
              "embed_dim": 192,
              "n_transformer_layers": 12,
              "n_attn_heads": 3,
              "ffn_dim": 192 * 4,
              "norm_layer": norm_layer,
              "pos_emb_drop_p": 0.1,
              "attn_dropout": 0.0,
              "ffn_dropout": 0.0,
              "dropout": dropout,
          }
      elif mode == "small":
          byteformer_config = {
              "embed_dim": 384,
              "n_transformer_layers": 12,
              "n_attn_heads": 6,
              "ffn_dim": 384 * 4,
              "norm_layer": norm_layer,
              "pos_emb_drop_p": 0.0,
              "attn_dropout": 0.0,
              "ffn_dropout": 0.0,
              "dropout": dropout,
          }
      elif mode == "base":
          byteformer_config = {
              "embed_dim": 768,
              "n_transformer_layers": 12,
              "n_attn_heads": 12,
              "ffn_dim": 768 * 4,
              "norm_layer": norm_layer,
              "pos_emb_drop_p": 0.0,
              "attn_dropout": 0.0,
              "ffn_dropout": 0.0,
              "dropout": dropout,
          }
      elif mode == "huge":
          byteformer_config = {
              "embed_dim": 1280,
              "n_transformer_layers": 32,
              "n_attn_heads": 20,  # each head dimension is 64
              "ffn_dim": 1280 * 4,
              "norm_layer": norm_layer,
              "pos_emb_drop_p": 0.0,
              "attn_dropout": 0.0,
              "ffn_dropout": 0.0,
              "dropout": dropout,
          }
      else:
          logger.error(
              "Got unsupported ByteFormer configuration: {}".format(
                  mode
              )
          )
      return byteformer_config
  
imports: import argparse
  from typing import Dict
  from utils import logger
label_desc: Use image node_id 1 for calling a global function with example usage: get_configuration(opts) and returns: byteformer_config