code: def make_prediction_texts_dense(
      batch: List[
          Tuple[
              List[str],  # texts
              List[List[float]],  # dense
          ]
      ],
  ) -> Tuple[List[str], List[List[float]]]:
      batchsize = len(batch)
  
      if batchsize == 0:
          raise RuntimeError(
              "Input batch must have at least 1 batch element"
          )
  
      flat_texts: List[str] = []
      flat_dense: List[List[float]] = []
  
      for i in range(batchsize):
          texts_element = batch[i][0]
          flat_texts.extend(texts_element)
          dense_element = batch[i][1]
          flat_dense.extend(dense_element)
  
          if len(texts_element) != len(dense_element):
              raise RuntimeError(
                  "This is not good. texts/dense client batch length mismatch"
              )
  
      if len(flat_texts) == 0:
          raise RuntimeError("This is not good. Empty request batch.")
  
      return (
          flat_texts,
          flat_dense,
      )
  
imports: from typing import Any, Dict, List, Optional, Tuple
  import torch
  from pytext.torchscript.tensorizer.tensorizer import ScriptTensorizer
label_desc: Use image node_id 22 for calling a global function with example usage: make_prediction_texts_dense(batch) and returns: flat_texts,  flat_dense