class_docstr: Nadaraya-Watson Kernel Regression
  
  This is basically a gaussian-weighted moving average of points
  
  Parameters
  ----------
  kernel : string
      kernel is either "gaussian", or one of the kernels available in
      sklearn.metrics.pairwise.
  h : float or array_like
      width of kernel.  If array, its length must be the number of
      dimensions in the training data
  
  Additional keyword arguments are passed to the kernel.
code: def fit(self, X, y, dy=1):
      self.X = np.asarray(X)
      self.y = np.asarray(y)
      self.dy = np.atleast_1d(dy)
      return self
  
file_path: astroML/astroML/linear_model/kernel_regression.py
imports: import numpy
  from sklearn.base import BaseEstimator
  from sklearn.metrics import pairwise_kernels
label_desc: Use image node_id 2 for calling the NadarayaWatson obj's underlying member method code with example usage: obj.fit(X, y, dy) and returns: self