class_docstr: This class is comparable to (and inspired by) the SwinEmbeddings class
  found in src/transformers/models/swin/modeling_swin.py.
code: def __init__(self, config: ConvNextConfig, **kwargs):
      super().__init__(**kwargs)
      self.patch_embeddings = tf.keras.layers.Conv2D(
          filters=config.hidden_sizes[0],
          kernel_size=config.patch_size,
          strides=config.patch_size,
          name="patch_embeddings",
          kernel_initializer=get_initializer(config.initializer_range),
          bias_initializer=tf.keras.initializers.Zeros(),
      )
      self.layernorm = tf.keras.layers.LayerNormalization(
          epsilon=1e-6, name="layernorm"
      )
      self.num_channels = config.num_channels
      self.config = config
  
imports: from __future__ import annotations
  from typing import List, Optional, Tuple, Union
  import numpy
  import tensorflow
  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 ...tf_utils import shape_list
  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 TFConvNextEmbeddings object from inherited base classes: tf.keras.layers with example: obj = TFConvNextEmbeddings(config)