import tensorflow as tf

      fields.InputDataFields.multiclass_scores]
  tensor_dict.pop(fields.InputDataFields.multiclass_scores, None)

  if fields.InputDataFields.groundtruth_confidences in tensor_dict:
    groundtruth_confidences = tensor_dict[
      fields.InputDataFields.groundtruth_confidences]
    # Map the confidences to the one-hot encoding of classes
    tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
      tf.reshape(groundtruth_confidences, [-1, 1]) *
      tensor_dict[fields.InputDataFields.groundtruth_classes])
  else:
    groundtruth_confidences = tf.ones_like(
      zero_indexed_groundtruth_classes, dtype=tf.float32)
    tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
      tensor_dict[fields.InputDataFields.groundtruth_classes])

