docstr: Returns the total number of points in the search space.
docstr_tok: ["Returns","the","total","number","of","points","in","the","search","space","."]
code: def get_grid_size(self) -> int:
      """Returns the total number of points in the search space."""
      if "max_prob" in self.confidence_type:
          return (
              len(self.exclude_blank)
              * len(self.aggregation)
              * ((len(self.confidence_type) - 1) * len(self.alpha) + 1)
          )
      return (
          len(self.exclude_blank)
          * len(self.aggregation)
          * len(self.confidence_type)
          * len(self.alpha)
      )
  
imports: import atexit
  import logging
  import os
  import random
  import sys
  import tempfile
  from copy import deepcopy
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import joblib
  import numpy
  import pytorch_lightning
  from omegaconf import MISSING, DictConfig, OmegaConf
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import confusion_matrix
  from sklearn.pipeline import Pipeline, make_pipeline
  from sklearn.preprocessing import StandardScaler
  from tqdm import tqdm
  from nemo.collections.asr.models.confidence_ensemble import ConfidenceEnsembleModel, ConfidenceSpec, compute_confidence, get_filtered_logprobs
  from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceConfig, ConfidenceMethodConfig, get_confidence_aggregation_bank, get_confidence_measure_bank
  from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
  from nemo.core.config import hydra_runner
label_desc: Use image node_id 1 for calling the TuneConfidenceConfig obj's underlying member method code with example usage: obj.get_grid_size() and returns: unknown, unknown