src/transformers/models/mobilevit/modeling_tf_mobilevit.py/TFMobileViTLayer/call
class TFMobileViTLayer:    def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
        # reduce spatial dimensions if needed
        if self.downsampling_layer:
            features = self.downsampling_layer(features, training=training)

        residual = features

        # local representation
        features = self.conv_kxk(features, training=training)
        features = self.conv_1x1(features, training=training)

        # convert feature map to patches
        patches, info_dict = self.unfolding(features)

        # learn global representations
        patches = self.transformer(patches, training=training)
        patches = self.layernorm(patches)

        # convert patches back to feature maps
        features = self.folding(patches, info_dict)

        features = self.conv_projection(features, training=training)
        features = self.fusion(tf.concat([residual, features], axis=-1), training=training)
        return features