code: def pad_predictions(predictions, init_net, net, num_classes):
      # This function will introduce two labels for beginning of sequence
      # And end of sequence, it will make the necessary udpates to the
      # the predictions blob
  
      low_score = -1000.0  # An arbitray very low number
      b_scores = np.array(
          [[low_score] * num_classes + [0, low_score]]
      ).astype(np.float32)
  
      e_scores = np.array(
          [[low_score] * num_classes + [low_score, 0]]
      ).astype(np.float32)
  
      b_scores = init_net.GivenTensorFill(
          [], "b_scores", shape=[1, num_classes + 2], values=b_scores
      )
      e_scores = init_net.GivenTensorFill(
          [], "e_scores", shape=[1, num_classes + 2], values=e_scores
      )
  
      zero_index = net.ConstantFill([], shape=[1], value=0)
      length = net.Gather([net.Shape([predictions]), zero_index])
      length = net.Cast(length, to="int32")
      t_range = net.LengthsRangeFill(length)
      padding = net.ConstantFill([t_range], value=low_score)
      padding = net.ExpandDims(padding, dims=[1])
      padded_predictions, _ = net.Concat(
          [predictions, padding, padding], outputs=2, axis=1
      )
      padded_predictions_concat, _ = net.Concat(
          [b_scores, padded_predictions, e_scores], outputs=2, axis=0
      )
      return padded_predictions_concat
  
file_path: pytorch/caffe2/python/crf.py
imports: import numpy
  from caffe2.python import brew, core, model_helper, recurrent
label_desc: Use image node_id 9 for calling the CRFWithLoss obj's underlying member method code with example usage: obj.pad_predictions(predictions, init_net, net, num_classes) and returns: padded_predictions_concat