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","."]
docstr: Initializes a _BootstrapSampleCombineFn.
  
  Args:
    num_bootstrap_samples: The expected number of samples computed per slice.
    skip_ci_metric_keys: Set of metric keys for which to skip confidence
      interval computation. For metric keys in this set, just the point
      estimate will be returned.
docstr_tok: ["Initializes","a","_BootstrapSampleCombineFn",".","Args",":","num_bootstrap_samples",":","The","expected","number","of","samples","computed","per","slice",".","skip_ci_metric_keys",":","Set","of","metric","keys","for","which","to","skip","confidence","interval","computation",".","For","metric","keys","in","this","set",",","just","the","point","estimate","will","be","returned","."]
code: def __init__(
      self,
      num_bootstrap_samples: int,
      skip_ci_metric_keys: Optional[Set[metric_types.MetricKey]] = None,
  ):
      """Initializes a _BootstrapSampleCombineFn.
  
      Args:
        num_bootstrap_samples: The expected number of samples computed per slice.
        skip_ci_metric_keys: Set of metric keys for which to skip confidence
          interval computation. For metric keys in this set, just the point
          estimate will be returned.
      """
      super().__init__(
          num_samples=num_bootstrap_samples,
          full_sample_id=_FULL_SAMPLE_ID,
          skip_ci_metric_keys=skip_ci_metric_keys,
      )
  
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 1 to create a new _BootstrapSampleCombineFn object from inherited base classes: confidence_intervals_util with example: obj = _BootstrapSampleCombineFn(num_bootstrap_samples, skip_ci_metric_keys)