code: def calculate_overall_stats(predictions, targets, train_set_metadata):
      overall_stats = {}
      confusion_matrix = ConfusionMatrix(
          targets, predictions[PREDICTIONS], labels=["False", "True"]
      )
      overall_stats["confusion_matrix"] = confusion_matrix.cm.tolist()
      overall_stats["overall_stats"] = confusion_matrix.stats()
      overall_stats[
          "per_class_stats"
      ] = confusion_matrix.per_class_stats()
      fpr, tpr, thresholds = roc_curve(
          targets, predictions[PROBABILITIES]
      )
      overall_stats["roc_curve"] = {
          "false_positive_rate": fpr.tolist(),
          "true_positive_rate": tpr.tolist(),
      }
      overall_stats["roc_auc_macro"] = roc_auc_score(
          targets, predictions[PROBABILITIES], average="macro"
      )
      overall_stats["roc_auc_micro"] = roc_auc_score(
          targets, predictions[PROBABILITIES], average="micro"
      )
      ps, rs, thresholds = precision_recall_curve(
          targets, predictions[PROBABILITIES]
      )
      overall_stats["precision_recall_curve"] = {
          "precisions": ps.tolist(),
          "recalls": rs.tolist(),
      }
      overall_stats[
          "average_precision_macro"
      ] = average_precision_score(
          targets, predictions[PROBABILITIES], average="macro"
      )
      overall_stats[
          "average_precision_micro"
      ] = average_precision_score(
          targets, predictions[PROBABILITIES], average="micro"
      )
      overall_stats[
          "average_precision_samples"
      ] = average_precision_score(
          targets, predictions[PROBABILITIES], average="samples"
      )
  
      return overall_stats
  
imports: import logging
  from typing import Dict, List, Tuple, Union
  import numpy
  import torch
  from ludwig.constants import BINARY, COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, PROC_COLUMN
  from ludwig.error import InputDataError
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.schema.features.binary_feature import BinaryInputFeatureConfig, BinaryOutputFeatureConfig
  from ludwig.types import FeatureConfigDict, FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import calibration, output_feature_utils, strings_utils
  from ludwig.utils.eval_utils import average_precision_score, ConfusionMatrix, precision_recall_curve, roc_auc_score, roc_curve
  from ludwig.utils.types import DataFrame, TorchscriptPreprocessingInput
label_desc: Use image node_id 10 for calling the BinaryOutputFeature obj's underlying member method code with example usage: obj.calculate_overall_stats(predictions, targets, train_set_metadata) and returns: overall_stats