class_docstr: Helper container which encapsulates standard slot metrics in task oriented learning
  (jga, slot_p, slot_r, etc).
  
  Due to differences in dialogue representations between tasks, the input is pre-
  parsed ground truth and predicted slot dictionaries.
class_docstr_tok: ["Helper","container","which","encapsulates","standard","slot","metrics","in","task","oriented","learning","(","jga",",","slot_p",",","slot_r",",","etc",")",".","Due","to","differences","in","dialogue","representations","between","tasks",",","the","input","is","pre-","parsed","ground","truth","and","predicted","slot","dictionaries","."]
code: def add_with_prefixes(self, name, value):
      self.add(name, value)
      for prefix in self.prefixes:
          self.add(f"{prefix}/{name}", value)
  
file_path: ParlAI/parlai/core/tod/teacher_metrics.py
imports: from typing import Optional, List, Dict, Any
  from parlai.core.metrics import AverageMetric, BleuMetric, F1Metric, Metric, Metrics
label_desc: Use image node_id 2 for calling the SlotMetrics obj's underlying member method code with example usage: obj.add_with_prefixes(name, value) without return types