class_docstr: Score distribution plot.
class_docstr_tok: ["Score","distribution","plot","."]
docstr: Initializes confusion matrix plot.
  
  Args:
    num_thresholds: Number of thresholds to use when discretizing the curve.
      Values must be > 1. Defaults to 1000.
    name: Metric name.
docstr_tok: ["Initializes","confusion","matrix","plot",".","Args",":","num_thresholds",":","Number","of","thresholds","to","use","when","discretizing","the","curve",".","Values","must","be",">","1",".","Defaults","to","1000.","name",":","Metric","name","."]
code: def __init__(
      self,
      num_thresholds: int = DEFAULT_NUM_THRESHOLDS,
      name: str = SCORE_DISTRIBUTION_PLOT_NAME,
  ):
      """Initializes confusion matrix plot.
  
      Args:
        num_thresholds: Number of thresholds to use when discretizing the curve.
          Values must be > 1. Defaults to 1000.
        name: Metric name.
      """
      super().__init__(
          metric_util.merge_per_key_computations(
              _confusion_matrix_plot
          ),
          num_thresholds=num_thresholds,
          name=name,
      )
  
file_path: model-analysis/tensorflow_model_analysis/metrics/score_distribution_plot.py
imports: import copy
  from typing import Any, Dict, Iterator, Optional, Tuple
  import numpy
  from tensorflow_model_analysis import constants
  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
label_desc: Use image node_id 1 to create a new ScoreDistributionPlot object from inherited base classes: metric_types with example: obj = ScoreDistributionPlot(num_thresholds, name)