code: def forward(self, sample_list):
      text = sample_list.input_ids
      mask = sample_list.input_mask
      segment = sample_list.segment_ids
  
      if self._is_direct_features_input:
          modal = sample_list.image_feature_0
      else:
          modal = sample_list.image
  
      text_embedding, modal_embedding = self.base(
          text, modal, [mask, segment]
      )
      embedding = torch.cat([text_embedding, modal_embedding], dim=-1)
      output = {}
      output["scores"] = self.classifier(embedding)
      return output
  
imports: import collections
  from copy import deepcopy
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.encoders import MultiModalEncoderBase
  from mmf.utils.build import build_classifier_layer
  from mmf.utils.modeling import get_bert_configured_parameters
label_desc: Use image node_id 5 for calling the ConcatBERT obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output