docstr: Do the forward pass
  :param batch:
  :return:
docstr_tok: ["Do","the","forward","pass",":","param","batch",":",":","return",":"]
code: def forward(self, batch: Dict[str, Any]) -> torch.tensor:
      """
      Do the forward pass
      :param batch:
      :return:
      """
      with torch.no_grad():
          batch = _prepare_batch(
              batch, device="cpu", non_blocking=False
          )
          model_inputs = {}
          for p, pdefault in self.forward_params.items():
              val = batch.get(p)
              if val is None:
                  if pdefault is None:
                      raise ValueError(f'missing model parameter "{p}"')
                  else:
                      val = pdefault
  
              model_inputs[p] = val
          y_pred = self.model(**model_inputs)
  
      return y_pred
  
file_path: transfer-nlp/transfer_nlp/plugins/predictors.py
imports: import inspect
  import logging
  from itertools import zip_longest
  from typing import Dict, List, Any
  import torch
  from ignite.utils import convert_tensor
  from transfer_nlp.loaders.vectorizers import Vectorizer
label_desc: Use image node_id 2 for calling the PredictorABC obj's underlying member method code with example usage: obj.forward(batch) and returns: y_pred