code: def forward(self, tokens, label=None):
      tokens = tokens["tokens"]["tokens"][:, 0 : self._max_tokens]
      embedded = self.embedder(tokens)
      logits = self.linear(torch.flatten(embedded).unsqueeze(0))
      probs = torch.nn.functional.softmax(logits, dim=-1)
      output_dict = {"logits": logits, "probs": probs}
      if label is not None:
          output_dict["loss"] = self._loss(
              logits, label.long().view(-1)
          )
      return output_dict
  
file_path: allennlp/allennlp/common/testing/interpret_test.py
imports: from allennlp.predictors import TextClassifierPredictor
  from allennlp.models.model import Model
  import torch
label_desc: Use image node_id 2 for calling the FakeModelForTestingInterpret obj's underlying member method code with example usage: obj.forward(tokens, label) and returns: output_dict