class_docstr: T5 Finetuning model in the same format as MegatronGPTSFTModel 
class_docstr_tok: ["T5","Finetuning","model","in","the","same","format","as","MegatronGPTSFTModel"]
code: def cast_for_metric(
      self,
      pred,
      label,
      metric_name,
      class_labels=None,
      labels_are_strings=False,
  ):
      if metric_name == "exact_string_match" or "rouge":
          return pred, label
      pred = pred.replace(" ", "")
      label = label.replace(" ", "")
  
      # Correlation metrics require casting to float.
      if metric_name in ["pearson_corr_coef", "spearman_corr_coef"]:
          # Text-to-text model predictions may not always be valid floating point numbers.
          try:
              pred = float(pred)
          except ValueError:
              pred = 0.0
  
          try:
              label = float(label)
          except ValueError:
              raise ValueError(f"Could not convert {label} to float.")
  
          pred = torch.FloatTensor([pred]).to(self.device)
          label = torch.FloatTensor([label]).to(self.device)
  
      # Other metrics require casting to integers.
      elif (
          metric_name in self._metrics_require_string2category_map
          and not labels_are_strings
      ):
          # Text-to-text model predictions may not always be valid integers.
          try:
              pred = int(pred)
          except ValueError:
              pred = 0
  
          try:
              label = int(label)
          except ValueError:
              raise ValueError(f"Could not convert {label} to int.")
  
          pred = torch.LongTensor([pred]).to(self.device)
          label = torch.LongTensor([label]).to(self.device)
  
      # If labels are strings, we need to convert them to indices for some metrics.
      elif (
          metric_name in self._metrics_require_string2category_map
          and labels_are_strings
      ):
          # Cast string labels to integers before computing the metric.
          if pred not in class_labels:
              pred = 0  # If the prediction is not in the class labels, use the first class label.
          else:
              pred = class_labels.index(pred)
          if label not in class_labels:
              raise ValueError(
                  f"Ground truth label {label} is not in the class labels list : {class_labels}"
              )
          label = class_labels.index(label)
          pred = torch.LongTensor([pred]).to(self.device)
          label = torch.LongTensor([label]).to(self.device)
      else:
          raise ValueError(f"Metric {metric_name} not supported.")
  
      return pred, label
  
imports: import itertools
  import json
  from typing import Dict, List
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.data import ConcatMapDataset
  from nemo.collections.common.metrics import MetricStringToTorchMetric
  from nemo.collections.common.metrics.classification_accuracy import ExactStringPerCategoryMatchMetric
  from nemo.collections.nlp.data.common.sequence_to_sequence_dataset import SequenceToSequenceDataset
  from nemo.collections.nlp.data.language_modeling.megatron.t5_sft_dataset import T5SFTDataset
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model, T5Sentinel
  from nemo.collections.nlp.modules.common.megatron.utils import get_iterator_k_split
  from nemo.collections.nlp.parts.mixins.nlp_adapter_mixins import NLPAdapterModelMixin
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.utils import AppState, logging
label_desc: Use image node_id 9 for calling the MegatronT5SFTModel obj's underlying member method code with example usage: obj.cast_for_metric(pred, label, metric_name, class_labels, labels_are_strings) and returns: pred,  label, pred,  label