class_docstr: RegNet Embeddings (stem) composed of a single aggressive convolution.
code: def call(self, pixel_values):
      num_channels = shape_list(pixel_values)[1]
      if tf.executing_eagerly() and num_channels != self.num_channels:
          raise ValueError(
              "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=num_channels)
      pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
      hidden_state = self.embedder(pixel_values)
      return hidden_state
  
imports: from typing import Dict, Optional, Tuple, Union
  import tensorflow
  from ...activations_tf import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 2 for calling the TFRegNetEmbeddings obj's underlying member method code with example usage: obj.call(pixel_values) and returns: hidden_state