docstr: Returns:
  
  Examples:
  
  ```python
  >>> from transformers import ConvNextFeatureExtractor, TFConvNextModel
  >>> from PIL import Image
  >>> import requests
  
  >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
  >>> image = Image.open(requests.get(url, stream=True).raw)
  
  >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224")
  >>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
  
  >>> inputs = feature_extractor(images=image, return_tensors="tf")
  >>> outputs = model(**inputs)
  >>> last_hidden_states = outputs.last_hidden_state
  ```
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code: def call(
      self,
      pixel_values: Optional[TFModelInputType] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training: bool = False,
  ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
      r"""
      Returns:
  
      Examples:
  
      ```python
      >>> from transformers import ConvNextFeatureExtractor, TFConvNextModel
      >>> from PIL import Image
      >>> import requests
  
      >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
      >>> image = Image.open(requests.get(url, stream=True).raw)
  
      >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224")
      >>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
  
      >>> inputs = feature_extractor(images=image, return_tensors="tf")
      >>> outputs = model(**inputs)
      >>> last_hidden_states = outputs.last_hidden_state
      ```"""
      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")
  
      outputs = self.convnext(
          pixel_values=pixel_values,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
  
      if not return_dict:
          return (outputs[0],) + outputs[1:]
  
      return TFBaseModelOutputWithPooling(
          last_hidden_state=outputs.last_hidden_state,
          pooler_output=outputs.pooler_output,
          hidden_states=outputs.hidden_states,
      )
  
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  import tensorflow
  from transformers import shape_list
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_convnext import ConvNextConfig
label_desc: Use image node_id 2 for calling the TFConvNextModel obj's underlying member method code with example usage: obj.call(pixel_values, output_hidden_states, return_dict, training) and returns: TFBaseModelOutputWithPooling, unknown