class_docstr: Flip count metric.
  
  Calculates the number of flips in prediction values for original and
  counterfactual examples (which have sensitive terms swapped, ablated etc.)
  
  It calculates:
  - positive to negative flips
  - negative to positive flips
  
  Other than flip counts, it also provides sample of example ids for which
  those flips are happening.
docstr: Confidence intervals for FlipCount.
  
  Confidence intervals capture uncertainty in a metric if it were computed on
  more examples. For now, confidence interval has been disabled.
  Returns:
    Whether to compute confidence intervals.
docstr_tok: ["Confidence","intervals","for","FlipCount",".","Confidence","intervals","capture","uncertainty","in","a","metric","if","it","were","computed","on","more","examples",".","For","now",",","confidence","interval","has","been","disabled",".","Returns",":","Whether","to","compute","confidence","intervals","."]
code: def compute_confidence_interval(self) -> bool:
      """Confidence intervals for FlipCount.
  
      Confidence intervals capture uncertainty in a metric if it were computed on
      more examples. For now, confidence interval has been disabled.
      Returns:
        Whether to compute confidence intervals.
      """
      return False
  
file_path: model-analysis/tensorflow_model_analysis/addons/fairness/metrics/counterfactual_fairness/flip_count.py
imports: import collections
  from typing import Any, Dict, List, Optional, Iterator, Sequence, Tuple
  import numpy
  from tensorflow_model_analysis.metrics import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.utils import model_util
  from tensorflow_model_analysis.utils import util
label_desc: Use image node_id 2 for calling the FlipCount obj's underlying member method code with example usage: obj.compute_confidence_interval() and returns: False