code: def __init__(self, config: ConvNextConfig, *inputs, **kwargs):
      super().__init__(config, *inputs, **kwargs)
  
      self.num_labels = config.num_labels
      self.convnext = TFConvNextMainLayer(config, name="convnext")
  
      # Classifier head
      self.classifier = tf.keras.layers.Dense(
          units=config.num_labels,
          kernel_initializer=get_initializer(config.initializer_range),
          bias_initializer="zeros",
          name="classifier",
      )
  
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 1 to create a new TFConvNextForImageClassification object from inherited base classes: TFConvNextPreTrainedModel, TFSequenceClassificationLoss with example: obj = TFConvNextForImageClassification(config)