class_docstr: Base class for label propagation module.
  
  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
  
  alpha : float
      Clamping factor
  
  max_iter : integer
      Change maximum number of iterations allowed
  
  tol : float
      Convergence tolerance: threshold to consider the system at steady
      state
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code: def _build_graph(self):  # pragma: no cover
      raise NotImplementedError(
          "Graph construction must be implemented"
          " to fit a label propagation model."
      )
  
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 BaseLabelPropagation obj's underlying member method code with example usage: obj._build_graph() without return types