code: def forward(
      self,
      pixel_values: Optional[torch.Tensor] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      if pixel_values is None:
          raise ValueError("You have to specify pixel_values")
  
      hidden_states = self.conv_stem(pixel_values)
  
      all_hidden_states = () if output_hidden_states else None
  
      for i, layer_module in enumerate(self.layer):
          hidden_states = layer_module(hidden_states)
  
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
      last_hidden_state = self.conv_1x1(hidden_states)
  
      if self.pooler is not None:
          pooled_output = torch.flatten(
              self.pooler(last_hidden_state), start_dim=1
          )
      else:
          pooled_output = None
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  last_hidden_state,
                  pooled_output,
                  all_hidden_states,
              ]
              if v is not None
          )
  
      return BaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=last_hidden_state,
          pooler_output=pooled_output,
          hidden_states=all_hidden_states,
      )
  
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 3 for calling the MobileNetV2Model obj's underlying member method code with example usage: obj.forward(pixel_values, output_hidden_states, return_dict) and returns: BaseModelOutputWithPoolingAndNoAttention, tuple