code: def prepare_data(self, sample_list, batch_size):
      # turn off teacher forcing during beam search
      # (otherwise one cannot run beam search on val set)
      self.teacher_forcing = (
          self.config.inference.type != "beam_search"
          and hasattr(sample_list, "text")
      )
      data = {}
      if self.teacher_forcing:
          caption_lengths, sort_ind = sample_list.caption_len.sort(
              dim=0, descending=True
          )
          data["decode_lengths"] = (caption_lengths - 1).tolist()
          sample_list.text = sample_list.text[sort_ind]
          sample_list.answers = sample_list.answers[sort_ind]
          sample_list.image_feature_0 = sample_list.image_feature_0[
              sort_ind
          ]
          data["texts"] = sample_list.text
          timesteps = max(data["decode_lengths"])
          sample_list.add_field("targets", sample_list.text[:, 1:])
      else:
          data["texts"] = sample_list.answers.new_full(
              (batch_size, 1), self.vocab.SOS_INDEX, dtype=torch.long
          )
          timesteps = self.text_processor.max_length
          sample_list.add_field(
              "targets", sample_list.answers[:, 0, 1:]
          )
      return data, sample_list, timesteps
  
file_path: mmf/mmf/models/butd.py
imports: import torch
  from mmf.common.registry import registry
  from mmf.models.pythia import Pythia
  from mmf.modules.layers import ClassifierLayer
label_desc: Use image node_id 7 for calling the BUTD obj's underlying member method code with example usage: obj.prepare_data(sample_list, batch_size) and returns: data,  sample_list,  timesteps