src/transformers/models/mobilevit/modeling_tf_mobilevit.py/TFMobileViTASPP/call
class TFMobileViTASPP:    def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
        # since the hidden states were transposed to have `(batch_size, channels, height, width)`
        # layout we transpose them back to have `(batch_size, height, width, channels)` layout.
        features = tf.transpose(features, perm=[0, 2, 3, 1])
        pyramid = []
        for conv in self.convs:
            pyramid.append(conv(features, training=training))
        pyramid = tf.concat(pyramid, axis=-1)

        pooled_features = self.project(pyramid, training=training)
        pooled_features = self.dropout(pooled_features, training=training)
        return pooled_features