code: def forward(self, sample_list):
      # BERT Based Encoders
      args = []
      if "input_ids" in sample_list:
          text = sample_list.input_ids
          args.append(sample_list.input_mask)
          args.append(sample_list.segment_ids)
      else:
          text = sample_list.text
  
      embedding = self.base(text, *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 UnimodalText obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output