code: def ConvNeXtXLarge(
      model_name="convnext_xlarge",
      include_top=True,
      include_preprocessing=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      return ConvNeXt(
          depths=MODEL_CONFIGS["xlarge"]["depths"],
          projection_dims=MODEL_CONFIGS["xlarge"]["projection_dims"],
          drop_path_rate=0.0,
          layer_scale_init_value=1e-6,
          default_size=MODEL_CONFIGS["xlarge"]["default_size"],
          model_name=model_name,
          include_top=include_top,
          include_preprocessing=include_preprocessing,
          weights=weights,
          input_tensor=input_tensor,
          input_shape=input_shape,
          pooling=pooling,
          classes=classes,
          classifier_activation=classifier_activation,
      )
  
imports: import numpy
  from keras import backend
  from keras import initializers
  from keras import layers
  from keras import ops
  from keras import random
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.layers.layer import Layer
  from keras.models import Functional
  from keras.models import Sequential
  from keras.ops import operation_utils
  from keras.utils import file_utils
label_desc: Use image node_id 9 for calling a global function with example usage: ConvNeXtXLarge(model_name, include_top, include_preprocessing, weights, input_tensor, input_shape, pooling, classes, classifier_activation) and returns: ConvNeXt