code: def ConvNeXtBlock(
      projection_dim,
      drop_path_rate=0.0,
      layer_scale_init_value=1e-6,
      name=None,
  ):
      """ConvNeXt block.
  
      References:
      - https://arxiv.org/abs/2201.03545
      - https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
  
      Notes:
          In the original ConvNeXt implementation (linked above), the authors use
          `Dense` layers for pointwise convolutions for increased efficiency.
          Following that, this implementation also uses the same.
  
      Args:
          projection_dim (int): Number of filters for convolution layers. In the
              ConvNeXt paper, this is referred to as projection dimension.
          drop_path_rate (float): Probability of dropping paths. Should be within
              [0, 1].
          layer_scale_init_value (float): Layer scale value.
              Should be a small float number.
          name: name to path to the keras layer.
  
      Returns:
          A function representing a ConvNeXtBlock block.
      """
      if name is None:
          name = "prestem" + str(backend.get_uid("prestem"))
  
      def apply(inputs):
          x = inputs
  
          x = layers.Conv2D(
              filters=projection_dim,
              kernel_size=7,
              padding="same",
              groups=projection_dim,
              name=name + "_depthwise_conv",
          )(x)
          x = layers.LayerNormalization(
              epsilon=1e-6, name=name + "_layernorm"
          )(x)
          x = layers.Dense(
              4 * projection_dim, name=name + "_pointwise_conv_1"
          )(x)
          x = layers.Activation("gelu", name=name + "_gelu")(x)
          x = layers.Dense(
              projection_dim, name=name + "_pointwise_conv_2"
          )(x)
  
          if layer_scale_init_value is not None:
              x = LayerScale(
                  layer_scale_init_value,
                  projection_dim,
                  name=name + "_layer_scale",
              )(x)
          if drop_path_rate:
              layer = StochasticDepth(
                  drop_path_rate, name=name + "_stochastic_depth"
              )
          else:
              layer = layers.Activation(
                  "linear", name=name + "_identity"
              )
  
          return inputs + layer(x)
  
      return apply
  
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 1 for calling a global function with example usage: ConvNeXtBlock(projection_dim, drop_path_rate, layer_scale_init_value, name) and returns: apply, unknown