class_docstr: Compute the argkmin of row vectors of X on the ones of Y with labels.
  
  For each row vector of X, computes the indices of k first the rows
  vectors of Y with the smallest distances. Computes weighted mode of labels.
  
  ArgKminClassMode is typically used to perform bruteforce k-nearest neighbors
  queries when the weighted mode of the labels for the k-nearest neighbors
  are required, such as in `predict` methods.
  
  This class is not meant to be instantiated, one should only use
  its :meth:`compute` classmethod which handles allocation and
  deallocation consistently.
docstr: Return True if the dispatcher can be used for the given parameters.
  
  Parameters
  ----------
  X : ndarray of shape (n_samples_X, n_features)
      The input array to be labelled.
  
  Y : ndarray of shape (n_samples_Y, n_features)
      The input array whose labels are provided through the `labels`
      parameter.
  
  metric : str, default='euclidean'
      The distance metric to use. For a list of available metrics, see
      the documentation of :class:`~sklearn.metrics.DistanceMetric`.
      Currently does not support `'precomputed'`.
  
  Returns
  -------
  True if the PairwiseDistancesReduction can be used, else False.
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code: def is_usable_for(cls, X, Y, metric) -> bool:
      """Return True if the dispatcher can be used for the given parameters.
  
      Parameters
      ----------
      X : ndarray of shape (n_samples_X, n_features)
          The input array to be labelled.
  
      Y : ndarray of shape (n_samples_Y, n_features)
          The input array whose labels are provided through the `labels`
          parameter.
  
      metric : str, default='euclidean'
          The distance metric to use. For a list of available metrics, see
          the documentation of :class:`~sklearn.metrics.DistanceMetric`.
          Currently does not support `'precomputed'`.
  
      Returns
      -------
      True if the PairwiseDistancesReduction can be used, else False.
      """
      return (
          ArgKmin.is_usable_for(X, Y, metric)
          # TODO: Support CSR matrices.
          and not issparse(X)
          and not issparse(Y)
          # TODO: implement Euclidean specialization with GEMM.
          and metric not in ("euclidean", "sqeuclidean")
      )
  
imports: from abc import abstractmethod
  from typing import List
  import numpy
  from scipy.sparse import issparse
  from ...None import get_config
  from .._dist_metrics import BOOL_METRICS, METRIC_MAPPING64
  from ._argkmin import ArgKmin32, ArgKmin64
  from ._argkmin_classmode import ArgKminClassMode32, ArgKminClassMode64
  from ._base import _sqeuclidean_row_norms32, _sqeuclidean_row_norms64
  from ._radius_neighbors import RadiusNeighbors32, RadiusNeighbors64
label_desc: Use image node_id 1 for calling the ArgKminClassMode obj's underlying member method code with example usage: obj.is_usable_for(cls, X, Y, metric) and returns: unknown