class_docstr: Example count.
  
  Note that although the example_count is independent of the model, this metric
  will be associated with a model for consistency with other metrics.
class_docstr_tok: ["Example","count",".","Note","that","although","the","example_count","is","independent","of","the","model",",","this","metric","will","be","associated","with","a","model","for","consistency","with","other","metrics","."]
docstr: Always disable confidence intervals for ExampleCount.
  
  Confidence intervals capture uncertainty in a metric if it were computed on
  more examples. For ExampleCount, this sort of uncertainty is not meaningful,
  so confidence intervals are disabled.
  
  Returns:
    Whether to compute confidence intervals.
docstr_tok: ["Always","disable","confidence","intervals","for","ExampleCount",".","Confidence","intervals","capture","uncertainty","in","a","metric","if","it","were","computed","on","more","examples",".","For","ExampleCount",",","this","sort","of","uncertainty","is","not","meaningful",",","so","confidence","intervals","are","disabled",".","Returns",":","Whether","to","compute","confidence","intervals","."]
code: def compute_confidence_interval(self) -> bool:
      """Always disable confidence intervals for ExampleCount.
  
      Confidence intervals capture uncertainty in a metric if it were computed on
      more examples. For ExampleCount, this sort of uncertainty is not meaningful,
      so confidence intervals are disabled.
  
      Returns:
        Whether to compute confidence intervals.
      """
      return False
  
file_path: model-analysis/tensorflow_model_analysis/metrics/example_count.py
imports: from typing import Optional, Dict, Iterable, List
  import apache_beam
  import numpy
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.utils import util
label_desc: Use image node_id 2 for calling the ExampleCount obj's underlying member method code with example usage: obj.compute_confidence_interval() and returns: False