class_docstr: Label Propagation classifier
  
  Read more in the :ref:`User Guide <label_propagation>`.
  
  Parameters
  ----------
  kernel : {'knn', 'rbf', callable}
      String identifier for kernel function to use or the kernel function
      itself. Only 'rbf' and 'knn' strings are valid inputs. The function
      passed should take two inputs, each of shape [n_samples, n_features],
      and return a [n_samples, n_samples] shaped weight matrix.
  
  gamma : float
      Parameter for rbf kernel
  
  n_neighbors : integer > 0
      Parameter for knn kernel
  
  max_iter : integer
      Change maximum number of iterations allowed
  
  tol : float
      Convergence tolerance: threshold to consider the system at steady
      state
  
  Attributes
  ----------
  X_ : array, shape = [n_samples, n_features]
      Input array.
  
  classes_ : array, shape = [n_classes]
      The distinct labels used in classifying instances.
  
  label_distributions_ : array, shape = [n_samples, n_classes]
      Categorical distribution for each item.
  
  transduction_ : array, shape = [n_samples]
      Label assigned to each item via the transduction.
  
  n_iter_ : int
      Number of iterations run.
  
  Examples
  --------
  >>> import numpy as np
  >>> from sklearn import datasets
  >>> from mars.learn.semi_supervised import LabelPropagation
  >>> label_prop_model = LabelPropagation()
  >>> iris = datasets.load_iris()
  >>> rng = np.random.RandomState(42)
  >>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
  >>> labels = np.copy(iris.target)
  >>> labels[random_unlabeled_points] = -1
  >>> label_prop_model.fit(iris.data, labels)
  LabelPropagation(...)
  
  References
  ----------
  Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data
  with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon
  University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf
  
  See Also
  --------
  LabelSpreading : Alternate label propagation strategy more robust to noise
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code: def fit(self, X, y, session=None, run_kwargs=None):
      return super().fit(X, y, session=session, run_kwargs=run_kwargs)
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import ConvergenceWarning
  from ...None import tensor
  from ...core import ExecutableTuple
  from ..base import ClassifierMixin
  from ..metrics.pairwise import rbf_kernel
  from ..neighbors.unsupervised import NearestNeighbors
  from ..utils import check_array
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import check_is_fitted, check_X_y
label_desc: Use image node_id 3 for calling the LabelPropagation obj's underlying member method code with example usage: obj.fit(X, y, session, run_kwargs) and returns: super