code: def forward(self, x: Tensor) -> Tensor:
      if self.stride == 1:
          x1, x2 = x.chunk(2, dim=1)
          out = self.cat.cat([x1, self.branch2(x2)], dim=1)
      else:
          out = self.cat.cat([self.branch1(x), self.branch2(x)], dim=1)
  
      out = shufflenetv2.channel_shuffle(out, 2)
  
      return out
  
imports: from functools import partial
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torchvision.models import shufflenetv2
  from ...transforms._presets import ImageClassification
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _IMAGENET_CATEGORIES
  from .._utils import _ovewrite_named_param, handle_legacy_interface
  from ..shufflenetv2 import ShuffleNet_V2_X0_5_Weights, ShuffleNet_V2_X1_0_Weights, ShuffleNet_V2_X1_5_Weights, ShuffleNet_V2_X2_0_Weights
  from .utils import _fuse_modules, _replace_relu, quantize_model
label_desc: Use image node_id 2 for calling the QuantizableInvertedResidual obj's underlying member method code with example usage: obj.forward(x) and returns: out