code: def get_data_t(self, t, data, batch_size_t, prev_output):
      if self.teacher_forcing:
          # Modify batch_size for timestep t
          batch_size_t = sum([l > t for l in data["decode_lengths"]])
      elif (
          prev_output is not None
          and self.config.inference.type == "greedy"
      ):
          # Adding t-1 output words to data["text"] for greedy decoding
          output_softmax = torch.log_softmax(prev_output, dim=1)
          _, indices = torch.max(output_softmax, dim=1, keepdim=True)
          data["texts"] = torch.cat(
              (data["texts"], indices.view(batch_size_t, 1)), dim=1
          )
  
      # Slice data based on batch_size at timestep t
      data["texts"] = data["texts"][:batch_size_t]
      if "state" in data:
          h1 = data["state"]["td_hidden"][0][:batch_size_t]
          c1 = data["state"]["td_hidden"][1][:batch_size_t]
          h2 = data["state"]["lm_hidden"][0][:batch_size_t]
          c2 = data["state"]["lm_hidden"][1][:batch_size_t]
      else:
          h1, c1 = self.init_hidden_state(data["texts"])
          h2, c2 = self.init_hidden_state(data["texts"])
      data["state"] = {"td_hidden": (h1, c1), "lm_hidden": (h2, c2)}
      registry.register(f"{h1.device}_lstm_state", data["state"])
  
      return data, batch_size_t
  
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 9 for calling the BUTD obj's underlying member method code with example usage: obj.get_data_t(t, data, batch_size_t, prev_output) and returns: data,  batch_size_t