code: def forward(self, features: torch.Tensor) -> torch.Tensor:
      features = self.first_conv(features)
      if self.expand_1x1 is not None:
          features = self.expand_1x1(features)
      features = self.conv_3x3(features)
      features = self.reduce_1x1(features)
      return features
  
imports: from typing import Optional, Union
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, SemanticSegmenterOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_mobilenet_v2 import MobileNetV2Config
label_desc: Use image node_id 2 for calling the MobileNetV2Stem obj's underlying member method code with example usage: obj.forward(features) and returns: features