class_docstr: emotion postprocess
class_docstr_tok: ["emotion","postprocess"]
docstr: collect scores and meta of results
docstr_tok: ["collect","scores","and","meta","of","results"]
code: def collect_results(self, prediction, result, scorefile):
      """collect scores and meta of results"""
      for filepath, clipid, label, score in zip(
          prediction["filepath"],
          prediction["clipid"],
          prediction["labels"],
          prediction["softmax"],
      ):
          # record predict score for ROC curve
          scorefile.write(
              "{}, {}, {}, {}\n".format(
                  filepath, clipid, label, score[self.positive_id]
              )
          )
          result[filepath].append(
              {"clipid": clipid, "label": label, "softmax": score}
          )
  
file_path: delta/delta/utils/postprocess/speech_cls_proc.py
imports: import os
  import pickle
  import collections
  import numpy
  from absl import logging
  from sklearn.metrics import confusion_matrix
  from delta import utils
  from delta.utils.postprocess.base_postproc import PostProc
  from delta.utils.register import registers
label_desc: Use image node_id 5 for calling the EmoPostProc obj's underlying member method code with example usage: obj.collect_results(prediction, result, scorefile) without return types