code: def forward(self, sample_list):
      # BERT Based Encoders
      args = []
      if self._is_direct_features_input:
          modal = sample_list.image_feature_0
          modal = torch.mean(modal, dim=1)
      else:
          modal = sample_list.image
  
      embedding = self.base(modal, *args)
      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
label_desc: Use image node_id 4 for calling the UnimodalModal obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output