def _postprocess_embeddings(self, prediction_dict, y_indices, x_indices):
    'Performs postprocessing on embedding predictions.\n\n    Args:\n      prediction_dict: a dictionary holding predicted tensors, returned from the\n        predict() method. This dictionary should contain embedding prediction\n        feature maps for tracking task.\n      y_indices: A [batch_size, max_detections] int tensor with y indices for\n        all object centers.\n      x_indices: A [batch_size, max_detections] int tensor with x indices for\n        all object centers.\n\n    Returns:\n      embeddings: A [batch_size, max_detection, reid_embed_size] float32\n        tensor with L2 normalized embeddings extracted from detection box\n        centers.\n    '
    embedding_predictions = prediction_dict[TRACK_REID][(- 1)]
    embeddings = predicted_embeddings_at_object_centers(embedding_predictions, y_indices, x_indices)
    (embeddings, _) = tf.linalg.normalize(embeddings, axis=(- 1))
    return embeddings
Performs postprocessing on embedding predictions.

Args:
  prediction_dict: a dictionary holding predicted tensors, returned from the
    predict() method. This dictionary should contain embedding prediction
    feature maps for tracking task.
  y_indices: A [batch_size, max_detections] int tensor with y indices for
    all object centers.
  x_indices: A [batch_size, max_detections] int tensor with x indices for
    all object centers.

Returns:
  embeddings: A [batch_size, max_detection, reid_embed_size] float32
    tensor with L2 normalized embeddings extracted from detection box
    centers.
def _postprocess_embeddings(self, prediction_dict, y_indices, x_indices):
    'Performs postprocessing on embedding predictions.\n\n    Args:\n      prediction_dict: a dictionary holding predicted tensors, returned from the\n        predict() method. This dictionary should contain embedding prediction\n        feature maps for tracking task.\n      y_indices: A [batch_size, max_detections] int tensor with y indices for\n        all object centers.\n      x_indices: A [batch_size, max_detections] int tensor with x indices for\n        all object centers.\n\n    Returns:\n      embeddings: A [batch_size, max_detection, reid_embed_size] float32\n        tensor with L2 normalized embeddings extracted from detection box\n        centers.\n    '
    embedding_predictions = prediction_dict[TRACK_REID][(- 1)]
    embeddings = predicted_embeddings_at_object_centers(embedding_predictions, y_indices, x_indices)
    (embeddings, _) = tf.linalg.normalize(embeddings, axis=(- 1))
    return embeddings