code: def forward(self, input_data: Union[DataLoader, List]) -> List:
      if isinstance(input_data, (DataLoader, list)):
          length = len(input_data)
      else:
          invalidInputError(
              False,
              "The input should be a DataLoader or a list of input batchs",
          )
  
      if self.ps is None:
          # run inference in current process directly
          from bigdl.nano.pytorch import InferenceOptimizer
  
          with InferenceOptimizer.get_context(self.model):
              outputs = [self.model(inputs) for inputs in input_data]
          return outputs
  
      else:
          if isinstance(input_data, DataLoader) and not isinstance(
              input_data.dataset, TensorDataset
          ):
              for idx in range(self.p_num):
                  self.send_queue.put(input_data)
          else:
              for idx_and_batch in enumerate(input_data):
                  self.send_queue.put(idx_and_batch)
  
          outputs = [None] * length
          for _i in range(length):
              idx, output = self.recv_queue.get()
              invalidOperationError(
                  not isinstance(output, Exception),
                  f"forward error: {output}\n",
              )
              outputs[idx] = output
          with self.next_idx.get_lock():
              self.next_idx.value = 0
  
      return outputs
  
file_path: BigDL/python/nano/src/bigdl/nano/pytorch/inference/multi_instance.py
imports: from typing import Union, List
  import torch
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  from bigdl.nano.utils.common import invalidInputError, invalidOperationError
  from bigdl.nano.pytorch.model import AcceleratedLightningModule
label_desc: Use image node_id 2 for calling the _MultiInstanceModel obj's underlying member method code with example usage: obj.forward(input_data) and returns: outputs, outputs