class_docstr: Creates histogram from labels, predictions, and example weights.
class_docstr_tok: ["Creates","histogram","from","labels",",","predictions",",","and","example","weights","."]
code: def __init__(
      self,
      key: metric_types.PlotKey,
      eval_config: Optional[config_pb2.EvalConfig],
      aggregation_type: Optional[metric_types.AggregationType],
      class_weights: Optional[Dict[int, float]],
      example_weighted: bool,
      num_buckets: int,
      left: float,
      right: float,
      prediction_based_bucketing: bool,
      fractional_labels: bool,
  ):
      self._key = key
      self._eval_config = eval_config
      self._aggregation_type = aggregation_type
      self._class_weights = class_weights
      self._example_weighted = example_weighted
      self._num_buckets = num_buckets
      self._left = left
      self._range = right - left
      self._fractional_labels = fractional_labels
      self._prediction_based_bucketing = prediction_based_bucketing
  
file_path: model-analysis/tensorflow_model_analysis/metrics/calibration_histogram.py
imports: import dataclasses
  import operator
  from typing import Dict, Iterable, List, Optional
  import apache_beam
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 1 to create a new _CalibrationHistogramCombiner object from inherited base classes: beam with example: obj = _CalibrationHistogramCombiner(key, eval_config, aggregation_type, class_weights, example_weighted, num_buckets, left, right, prediction_based_bucketing, fractional_labels)