code: def forward(self, sample_list):
      # Stores the output probabilites.
      scores = sample_list.answers.new_ones(
          (
              sample_list.answers.size(0),
              self.text_processor.max_length,
              self.vocab_size,
          ),
          dtype=torch.float,
      )
  
      if self.config["inference"]["type"] in [
          "beam_search",
          "nucleus_sampling",
      ]:
          decoder = registry.get_decoder_class(
              self.config["inference"]["type"]
          )(self.vocab, self.config)
          sample_list = decoder.init_batch(sample_list)
  
      batch_size = sample_list.image_feature_0.size(0)
      data, sample_list, timesteps = self.prepare_data(
          sample_list, batch_size
      )
      output = None
      batch_size_t = batch_size
      for t in range(timesteps):
          data, batch_size_t = self.get_data_t(
              t, data, batch_size_t, output
          )
          if self.config.inference.type in [
              "beam_search",
              "nucleus_sampling",
          ]:
              pi_t = data["texts"]
          else:
              pi_t = data["texts"][:, t].unsqueeze(-1)
          embedding = self.word_embedding(pi_t)
          attention_feature, _ = self.process_feature_embedding(
              "image",
              sample_list,
              embedding[:, 0, :],
              batch_size_t=batch_size_t,
          )
          output = self.classifier(attention_feature)
          # Compute decoding
          if self.config.inference.type in [
              "beam_search",
              "nucleus_sampling",
          ]:
              finish, data, batch_size_t = decoder.decode(
                  t, data, output
              )
              if finish:
                  break
          else:
              scores[:batch_size_t, t] = output
  
      model_output = {}
      if self.config.inference.type in [
          "beam_search",
          "nucleus_sampling",
      ]:
          results = decoder.get_result()
          results = torch.nn.functional.pad(
              results,
              (0, self.text_processor.max_length - results.size()[-1]),
              "constant",
              0,
          )
          model_output["captions"] = results
          model_output["losses"] = {}
          loss_key = "{}/{}".format(
              sample_list.dataset_name, sample_list.dataset_type
          )
          # Add a dummy loss so that loss calculation is not required
          model_output["losses"][
              loss_key + "/dummy_loss"
          ] = torch.zeros(batch_size, device=sample_list.answers.device)
      else:
          model_output["scores"] = scores
  
      return model_output
  
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 10 for calling the BUTD obj's underlying member method code with example usage: obj.forward(sample_list) and returns: model_output