class_docstr: This class is comparable to (and inspired by) the SwinEmbeddings class
  found in src/transformers/models/swin/modeling_swin.py.
code: def call(self, pixel_values):
      if isinstance(pixel_values, dict):
          pixel_values = pixel_values["pixel_values"]
  
      tf.debugging.assert_equal(
          shape_list(pixel_values)[1],
          self.num_channels,
          message="Make sure that the channel dimension of the pixel values match with the one set in the configuration.",
      )
  
      # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
      # So change the input format from `NCHW` to `NHWC`.
      # shape = (batch_size, in_height, in_width, in_channels)
      pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
  
      embeddings = self.patch_embeddings(pixel_values)
      embeddings = self.layernorm(embeddings)
      return embeddings
  
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 2 for calling the TFConvNextEmbeddings obj's underlying member method code with example usage: obj.call(pixel_values) and returns: embeddings