class_docstr: Represents a kernel based density estimator. The estimator uses the sklearn KernelDensity class internally.
class_docstr_tok: ["Represents","a","kernel","based","density","estimator",".","The","estimator","uses","the","sklearn","KernelDensity","class","internally","."]
code: def fit(self, X: np.ndarray) -> None:
      X = shape_into_2d(X)
      self._validate_data(X)
  
      bandwidth = np.std(X) * np.power(4 / 3 / X.shape[0], 1 / 5)
  
      self._kde_model = KernelDensity(
          kernel="gaussian", bandwidth=bandwidth
      ).fit(X.reshape(-1, 1))
  
file_path: dowhy/dowhy/gcm/density_estimators.py
imports: from typing import Optional
  import numpy
  from sklearn.mixture import BayesianGaussianMixture
  from sklearn.neighbors import KernelDensity
  from dowhy.gcm.density_estimator import DensityEstimator
  from dowhy.gcm.util.general import shape_into_2d
label_desc: Use image node_id 2 for calling the KernelDensityEstimator1D obj's underlying member method code with example usage: obj.fit(X) without return types