class_docstr: Mixin for linear classifiers.
  
  Handles prediction for sparse and dense X.
class_docstr_tok: ["Mixin","for","linear","classifiers",".","Handles","prediction","for","sparse","and","dense","X","."]
docstr: Predict class labels for samples in X.
  
  Parameters
  ----------
  X : array-like or sparse matrix, shape (n_samples, n_features)
      Samples.
  
  Returns
  -------
  C : array, shape [n_samples]
      Predicted class label per sample.
docstr_tok: ["Predict","class","labels","for","samples","in","X.","Parameters","--","--","--","--","--","X",":","array-like","or","sparse","matrix",",","shape","(","n_samples",",","n_features",")","Samples",".","Returns","--","--","--","-","C",":","array",",","shape","[","n_samples","]","Predicted","class","label","per","sample","."]
code: def predict(self, X):
      """
      Predict class labels for samples in X.
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape (n_samples, n_features)
          Samples.
  
      Returns
      -------
      C : array, shape [n_samples]
          Predicted class label per sample.
      """
      scores = self.decision_function(X)
      indices = scores.argmax(axis=1)
      return self.classes_[indices].execute()
  
imports: import numbers
  from abc import ABCMeta, abstractmethod
  from numpy.linalg import LinAlgError
  from sklearn.utils.validation import check_is_fitted, _deprecate_positional_args
  from sklearn.base import MultiOutputMixin
  from ...None import execute
  from ...None import tensor
  from ...tensor.datasource import tensor
  from ..base import BaseEstimator, RegressorMixin, ClassifierMixin
  from ..preprocessing import normalize
  from ..utils.validation import _check_sample_weight, check_array, FLOAT_DTYPES
label_desc: Use image node_id 2 for calling the LinearClassifierMixin obj's underlying member method code with example usage: obj.predict(X) and returns: self