code: def forward(self, inputs, data_samples, mode="tensor"):
      labels = torch.stack(data_samples)
      inputs = torch.stack(inputs)
      outputs = self.linear(inputs)
      if mode == "tensor":
          return outputs
      elif mode == "loss":
          loss = (labels - outputs).sum()
          outputs = dict(loss=loss)
          return outputs
      else:
          return outputs
  
imports: import os.path
  import tempfile
  from unittest import TestCase
  import torch
  import torch.nn
  from mmengine.evaluator import BaseMetric
  from mmengine.model import BaseModel
  from mmengine.optim import OptimWrapper
  from mmengine.registry import MODEL_WRAPPERS
  from mmengine.runner import Runner
  from torch.utils.data import Dataset
  from mmdet.registry import DATASETS
  from mmdet.utils import register_all_modules
label_desc: Use image node_id 2 for calling the ToyModel obj's underlying member method code with example usage: obj.forward(inputs, data_samples, mode) and returns: outputs, outputs, outputs