src/transformers/models/regnet/modeling_tf_regnet.py/TFRegNetEmbeddings/call
class TFRegNetEmbeddings:    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