code: def forward(
      self,
      outputs,
      cls_output=None,
      attention_mask=None,
      return_dict=False,
      **kwargs,
  ):
      logits = super().forward(outputs[0])
      outputs = (logits,) + outputs[2:]
      return outputs
  
file_path: adapters/tests/test_adapter_custom_head.py
imports: import tempfile
  import unittest
  import torch
  from adapters import AutoAdapterModel
  from adapters.heads import ClassificationHead, PredictionHead
  from transformers import AutoConfig
  from transformers.testing_utils import require_torch, torch_device
  from .test_adapter import ids_tensor
label_desc: Use image node_id 2 for calling the CustomHead obj's underlying member method code with example usage: obj.forward(outputs, cls_output, attention_mask, return_dict) and returns: outputs