class_docstr: Computes the bootstrap standard error for each metric from samples.
class_docstr_tok: ["Computes","the","bootstrap","standard","error","for","each","metric","from","samples","."]
code: def extract_output(
      self,
      accumulator: confidence_intervals_util.SampleCombineFn.SampleAccumulator,
  ) -> metric_types.MetricsDict:
      accumulator = self._validate_accumulator(accumulator)
      result = {}
      dof = self._num_samples - 1
      for key, point_estimate in accumulator.point_estimates.items():
          if key not in accumulator.metric_samples:
              result[key] = point_estimate
          else:
              mean, std_error = confidence_intervals_util.mean_and_std(
                  accumulator.metric_samples[key], ddof=1
              )
              result[key] = types.ValueWithTDistribution(
                  sample_mean=mean,
                  sample_standard_deviation=std_error,
                  unsampled_value=point_estimate,
                  sample_degrees_of_freedom=dof,
              )
      return result
  
file_path: model-analysis/tensorflow_model_analysis/evaluators/poisson_bootstrap.py
imports: from typing import Any, Optional, Set, Tuple, TypeVar
  import apache_beam
  import numpy
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.evaluators import confidence_intervals_util
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.slicer import slicer_lib
  from tensorflow_model_analysis.utils import beam_util
label_desc: Use image node_id 2 for calling the _BootstrapSampleCombineFn obj's underlying member method code with example usage: obj.extract_output(accumulator) and returns: result