code: def _heatmap_from_feature(self, features):
      n_landmarks = tf.to_int32(features["n_landmarks"])
      gt_lms = tf.decode_raw(features["gt_pts"], tf.float32)
      mask_index = tf.decode_raw(features["mask_index"], tf.float32)
      gt_mask = tf.decode_raw(features["gt_mask"], tf.float32)
      image_height = tf.to_int32(features["height"])
      image_width = tf.to_int32(features["width"])
  
      gt_lms = tf.reshape(gt_lms, (n_landmarks, 2))
      gt_heatmap = lms_to_heatmap(
          gt_lms, image_height, image_width, n_landmarks, mask_index
      )
      gt_heatmap = tf.transpose(gt_heatmap, perm=[1, 2, 0])
  
      return gt_heatmap, gt_lms, n_landmarks, mask_index, gt_mask
  
file_path: insightface/reconstruction/ostec/external/landmark_detector/data_provider.py
imports: import tensorflow
  import numpy
  from menpo.transform import Translation
  from external.landmark_detector.flags import FLAGS
label_desc: Use image node_id 5 for calling the ProtobuffProvider obj's underlying member method code with example usage: obj._heatmap_from_feature(features) and returns: gt_heatmap,  gt_lms,  n_landmarks,  mask_index,  gt_mask