class_docstr: This imputes the values of missing features using the values of the observed features.
  
  Unlike Independent, Gaussian imputes missing values based on correlations with observed data points.
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docstr: Build a Partition masker with the given background data and clustering.
  
  Parameters
  ----------
  data : numpy.ndarray, pandas.DataFrame or {"mean: numpy.ndarray, "cov": numpy.ndarray} dictionary
      The background dataset that is used for masking.
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code: def __init__(self, data, method="linear"):
      """Build a Partition masker with the given background data and clustering.
  
      Parameters
      ----------
      data : numpy.ndarray, pandas.DataFrame or {"mean: numpy.ndarray, "cov": numpy.ndarray} dictionary
          The background dataset that is used for masking.
      """
      if data is dict and "mean" in data:
          self.mean = data.get("mean", None)
          self.cov = data.get("cov", None)
          data = np.expand_dims(data["mean"], 0)
  
      self.data = data
      self.method = method
  
imports: import logging
  import numpy
  import pandas
  from numba import njit
  from ..None import utils
  from .._serializable import Deserializer, Serializer
  from ..utils import MaskedModel
  from ..utils._exceptions import DimensionError, InvalidClusteringError
  from ._masker import Masker
label_desc: Use image node_id 1 to create a new Impute object from inherited base classes: Masker with example: obj = Impute(data, method)