code: def _truncate_texts(setfit_model, max_seq_len, *datasets):
      tokenizer = setfit_model.model_body.tokenizer
      datasets_out = []
      for dataset in datasets:
          token_list = [
              tokenizer.encode(text, verbose=False)
              for text in dataset.x
          ]
          if any([len(tokens) > max_seq_len for tokens in token_list]):
              x_new = [
                  tokenizer.convert_tokens_to_string(
                      tokenizer.convert_ids_to_tokens(
                          tokenizer.encode(
                              text,
                              max_length=max_seq_len,
                              truncation=True,
                              add_special_tokens=False,
                          )
                      )
                  )
                  for text in dataset.x
              ]
              dataset_copy = dataset.clone()
              dataset_copy.x = x_new
              datasets_out.append(dataset_copy)
          else:
              datasets_out.append(dataset)
      return datasets_out
  
label_desc: Use image node_id 4 for calling a global function with example usage: _truncate_texts(setfit_model, max_seq_len) and returns: datasets_out