docstr: Returns:
  
  Examples:
  
  ```python
  >>> from transformers import AutoImageProcessor, AutoBackbone
  >>> import torch
  >>> from PIL import Image
  >>> import requests
  
  >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  >>> image = Image.open(requests.get(url, stream=True).raw)
  
  >>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
  >>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224")
  
  >>> inputs = processor(image, return_tensors="pt")
  >>> outputs = model(**inputs)
  ```
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code: def forward(
      self,
      pixel_values: torch.Tensor,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> BackboneOutput:
      """
      Returns:
  
      Examples:
  
      ```python
      >>> from transformers import AutoImageProcessor, AutoBackbone
      >>> import torch
      >>> from PIL import Image
      >>> import requests
  
      >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
      >>> image = Image.open(requests.get(url, stream=True).raw)
  
      >>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
      >>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224")
  
      >>> inputs = processor(image, return_tensors="pt")
      >>> outputs = model(**inputs)
      ```"""
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
  
      embedding_output = self.embeddings(pixel_values)
  
      outputs = self.encoder(
          embedding_output,
          output_hidden_states=True,
          return_dict=return_dict,
      )
  
      hidden_states = (
          outputs.hidden_states if return_dict else outputs[1]
      )
  
      feature_maps = ()
      for stage, hidden_state in zip(self.stage_names, hidden_states):
          if stage in self.out_features:
              hidden_state = self.hidden_states_norms[stage](
                  hidden_state
              )
              feature_maps += (hidden_state,)
  
      if not return_dict:
          output = (feature_maps,)
          if output_hidden_states:
              output += (hidden_states,)
          return output
  
      return BackboneOutput(
          feature_maps=feature_maps,
          hidden_states=hidden_states if output_hidden_states else None,
          attentions=None,
      )
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  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 ...utils.backbone_utils import BackboneMixin
  from .configuration_convnext import ConvNextConfig
label_desc: Use image node_id 2 for calling the ConvNextBackbone obj's underlying member method code with example usage: obj.forward(pixel_values, output_hidden_states, return_dict) and returns: BackboneOutput, output