import tensorflow as tf


    pred_max = tf.reduce_max(predictions, axis=-1)
    pred_indices = tf.argmax(predictions, axis=-1)
    pred_x, pred_y = tf.cast(tf.floormod(pred_indices, heatmap_size), tf.float32), tf.cast(tf.floordiv(pred_indices, heatmap_size), tf.float32)

    width, height = tf.cast(width, tf.float32), tf.cast(height, tf.float32)
    pred_x, pred_y = pred_x * width / tf.cast(heatmap_size, tf.float32), pred_y * height / tf.cast(heatmap_size, tf.float32)

    if clip_at_zero:
      pred_x, pred_y =  pred_x * tf.cast(pred_max>0, tf.float32), pred_y * tf.cast(pred_max>0, tf.float32)
      pred_x = pred_x * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (width / 2.)
      pred_y = pred_y * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (height / 2.)

    if config.PRED_DEBUG:
      pred_indices_ = tf.squeeze(pred_indices)
      image_ = tf.squeeze(image) * 255.
      pred_heatmap = tf.one_hot(pred_indices_, heatmap_size*heatmap_size, on_value=1., off_value=0., axis=-1, dtype=tf.float32)

