class_docstr: Display feature correlations significance matrix.
  
  This report renders correlations significance matrix in a form of heatmap.
  The details of the report to be rendered depend on the configuration of
  :py:class:`~autogluon.eda.analysis.interaction.Correlation` and
  :py:class:`~autogluon.eda.analysis.interaction.CorrelationSignificance` analyses.
  
  Parameters
  ----------
  headers: bool, default = False
      if `True` then render headers
  namespace: Optional[str], default = None
      namespace to use; can be nested like `ns_a.ns_b.ns_c`
  fig_args: Optional[Dict[str, Any]] = None,
      kwargs to pass into chart figure
  
  See Also
  --------
  :py:class:`~autogluon.eda.analysis.interaction.Correlation`
  :py:class:`~autogluon.eda.analysis.interaction.CorrelationSignificance`
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code: def can_handle(self, state: AnalysisState) -> bool:
      return "significance_matrix" in state
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Optional, Tuple, Type, Union
  import matplotlib.pyplot
  import numpy
  import pandas
  import seaborn
  from scipy import stats
  from scipy.cluster import hierarchy
  from sklearn.inspection import PartialDependenceDisplay
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_DATETIME, R_FLOAT, R_INT, R_OBJECT
  from ..state import AnalysisState
  from .base import AbstractVisualization
  from .jupyter import JupyterMixin
label_desc: Use image node_id 1 for calling the CorrelationSignificanceVisualization obj's underlying member method code with example usage: obj.can_handle(state) and returns: unknown