code: def forward(self, q, k, v=None, mask=None):
      a, b = q + self.d, 2 * k - 2 * self.d
      if v is not None and mask is not None:
          return a, b, v, mask
      elif v is not None:
          return a, b, v
      elif mask is not None:
          return a, b, mask
      return a, b
  
imports: import random
  import pytest
  import torch
  import torch.nn
  from torch.utils.data import TensorDataset, DataLoader
  import nni
  from nni.mutable import frozen
  from nni.nas.hub.pytorch.nasbench201 import OPS_WITH_STRIDE
  from nni.nas.nn.pytorch import Repeat
  from nni.nas.oneshot.pytorch.supermodule.base import BaseSuperNetModule
  from nni.nas.oneshot.pytorch.supermodule.proxyless import ProxylessMixedLayer, ProxylessMixedInput, ProxylessMixedRepeat, _iter_tensors
  from pytorch_lightning import LightningModule, Trainer
label_desc: Use image node_id 2 for calling the MultiInputLayer obj's underlying member method code with example usage: obj.forward(q, k, v, mask) and returns: a,  b, a,  b,  v,  mask, a,  b,  v, a,  b,  mask