class_docstr: Confusion matrix 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.
    **kwargs: (Optional) Additional args to pass along to init (and eventually
      on to _confusion_matrix_plot). These kwargs are useful for subclasses to
      pass information from their init to the create_computation_fn.
code: def __init__(
      self,
      num_thresholds: int = DEFAULT_NUM_THRESHOLDS,
      name: str = CONFUSION_MATRIX_PLOT_NAME,
      **kwargs,
  ):
      """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.
        **kwargs: (Optional) Additional args to pass along to init (and eventually
          on to _confusion_matrix_plot). These kwargs are useful for subclasses to
          pass information from their init to the create_computation_fn.
      """
      super().__init__(
          metric_util.merge_per_key_computations(
              self._confusion_matrix_plot
          ),
          num_thresholds=num_thresholds,
          name=name,
          **kwargs,
      )
  
file_path: model-analysis/tensorflow_model_analysis/metrics/confusion_matrix_plot.py
imports: from typing import Any, Dict, Optional, List
  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 ConfusionMatrixPlot object from inherited base classes: metric_types with example: obj = ConfusionMatrixPlot(num_thresholds, name)