code: def get_score(self, ground_truth, predicted_text):
      corrects = 0
      for pred, label in zip(predicted_text, ground_truth):
          if pred == label:
              corrects += 1
  
      val_acc = corrects / len(ground_truth)
      return {"accuracy": torch.tensor(val_acc)}
  
file_path: NeMo/nemo/collections/nlp/metrics/prompt_learning_metrics.py
imports: from abc import ABC, abstractmethod
  import numpy
  import torch
  from rouge_score import rouge_scorer
  from sacrebleu import BLEU
label_desc: Use image node_id 1 for calling the AccuracyScore obj's underlying member method code with example usage: obj.get_score(ground_truth, predicted_text) and returns: dict