docstr: Causal Tree exporter for matplotlib
  Source: https://github.com/scikit-learn/scikit-learn/blob/1.0.X/sklearn/tree/_export.py
  Args:
      causal_tree: CausalTreeRegressor
              The causal tree to be plotted
       max_depth:  int, default=None
          The maximum depth of the representation. If None, the tree is fully generated.
      feature_names: list of strings, default=None
              Names of each of the features.
              If None, generic names will be used ("X[0]", "X[1]", ...).
      class_names:    list of str or bool, default=None
              Names of each of the target classes in ascending numerical order.
              Only relevant for classification and not supported for multi-output.
              If ``True``, shows a symbolic representation of the class name.
      label:  {'all', 'root', 'none'}, default='all'
              Whether to show informative labels for impurity, etc.
              Options include 'all' to show at every node, 'root' to show only at
              the top root node, or 'none' to not show at any node.
      filled: bool, default=False
              When set to ``True``, paint nodes to indicate extremity of node values
      impurity: bool, default=True
              When set to ``True``, show the impurity at each node.
       groups_count: bool, default=True
          Add the number of treatment and control groups
      treatment_groups: tuple, default=(0, 1)
              Treatment and control groups labels
      node_ids: bool, default=False
              When set to ``True``, show the ID number on each node.
      proportion: bool, default=False
          When set to ``True``, change the display of 'values' and/or 'samples'
          to be proportions and percentages respectively.
      rounded: bool, default=False
              When set to ``True``, draw node boxes with rounded corners and use
              Helvetica fonts instead of Times-Roman.
      precision: int, default=3
              Number of digits of precision for floating point in the values of
              impurity, threshold and value attributes of each node.
      fontsize: int, default=None
              Size of text font. If None, determined automatically to fit figure.
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code: def __init__(
      self,
      causal_tree: CausalTreeRegressor,
      max_depth: int,
      feature_names: list,
      class_names: list,
      label: str,
      filled: bool,
      impurity: bool,
      groups_count: bool,
      treatment_groups: tuple,
      node_ids: bool,
      proportion: bool,
      rounded: bool,
      precision: int,
      fontsize: int,
  ):
      """
      Causal Tree exporter for matplotlib
      Source: https://github.com/scikit-learn/scikit-learn/blob/1.0.X/sklearn/tree/_export.py
      Args:
          causal_tree: CausalTreeRegressor
                  The causal tree to be plotted
           max_depth:  int, default=None
              The maximum depth of the representation. If None, the tree is fully generated.
          feature_names: list of strings, default=None
                  Names of each of the features.
                  If None, generic names will be used ("X[0]", "X[1]", ...).
          class_names:    list of str or bool, default=None
                  Names of each of the target classes in ascending numerical order.
                  Only relevant for classification and not supported for multi-output.
                  If ``True``, shows a symbolic representation of the class name.
          label:  {'all', 'root', 'none'}, default='all'
                  Whether to show informative labels for impurity, etc.
                  Options include 'all' to show at every node, 'root' to show only at
                  the top root node, or 'none' to not show at any node.
          filled: bool, default=False
                  When set to ``True``, paint nodes to indicate extremity of node values
          impurity: bool, default=True
                  When set to ``True``, show the impurity at each node.
           groups_count: bool, default=True
              Add the number of treatment and control groups
          treatment_groups: tuple, default=(0, 1)
                  Treatment and control groups labels
          node_ids: bool, default=False
                  When set to ``True``, show the ID number on each node.
          proportion: bool, default=False
              When set to ``True``, change the display of 'values' and/or 'samples'
              to be proportions and percentages respectively.
          rounded: bool, default=False
                  When set to ``True``, draw node boxes with rounded corners and use
                  Helvetica fonts instead of Times-Roman.
          precision: int, default=3
                  Number of digits of precision for floating point in the values of
                  impurity, threshold and value attributes of each node.
          fontsize: int, default=None
                  Size of text font. If None, determined automatically to fit figure.
      """
      super().__init__(
          max_depth,
          feature_names,
          class_names,
          label,
          filled,
          impurity,
          node_ids,
          proportion,
          rounded,
          precision,
          fontsize,
      )
      self.causal_tree = causal_tree
      self.groups_count = groups_count
      self.treatment_groups = treatment_groups
  
imports: from collections import defaultdict
  import matplotlib.pyplot
  import numpy
  import pydotplus
  import seaborn
  from sklearn.tree import _tree
  from sklearn.tree._export import _MPLTreeExporter, _color_brew
  from sklearn.utils.validation import check_is_fitted
  from .None import CausalTreeRegressor
  from .utils import get_tree_leaves_mask
label_desc: Use image node_id 1 to create a new _MPLCTreeExporter object from inherited base classes: _MPLTreeExporter with example: obj = _MPLCTreeExporter(causal_tree, max_depth, feature_names, class_names, label, filled, impurity, groups_count, treatment_groups, node_ids, proportion, rounded, precision, fontsize)