class_docstr: Model used to check different inputs and outputs
class_docstr_tok: ["Model","used","to","check","different","inputs","and","outputs"]
code: def forward(self, x1, x2=None):
      out1 = self.conv1(x1)
      out2 = self.conv2(x2)
      if self.multiout:
          return out1, out2
      return out1 + out2
  
imports: import pytest
  import torch
  import torch.nn
  from torch.utils.checkpoint import checkpoint
  from fairscale.fair_dev.testing.testing import skip_if_no_cuda
  from fairscale.internal import torch_version
  from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper, disable_checkpointing
  from fairscale.nn.misc import FlattenParamsWrapper
  from fairscale.nn.misc import checkpoint_wrapper
label_desc: Use image node_id 2 for calling the MultiinMultioutModel obj's underlying member method code with example usage: obj.forward(x1, x2) and returns: unknown, out1,  out2