src/transformers/models/resnet/modeling_tf_resnet.py/TFResNetEmbeddings/call
class TFResNetEmbeddings:    def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
        _, _, _, num_channels = shape_list(pixel_values)
        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."
            )
        hidden_state = pixel_values
        hidden_state = self.embedder(hidden_state)
        hidden_state = tf.pad(hidden_state, [[0, 0], [1, 1], [1, 1], [0, 0]])
        hidden_state = self.pooler(hidden_state)
        return hidden_state