code: def linearsvc(embeds, labels):
      x = embeds.cpu().numpy()
      y = labels.cpu().numpy()
      params = {"C": [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
      kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=None)
      accuracies = []
      for train_index, test_index in kf.split(x, y):
          x_train, x_test = x[train_index], x[test_index]
          y_train, y_test = y[train_index], y[test_index]
          classifier = GridSearchCV(
              LinearSVC(), params, cv=5, scoring="accuracy", verbose=0
          )
          classifier.fit(x_train, y_train)
          accuracies.append(
              accuracy_score(y_test, classifier.predict(x_test))
          )
      return np.mean(accuracies), np.std(accuracies)
  
file_path: dgl/examples/pytorch/mvgrl/graph/utils.py
imports: import math
  import numpy
  import torch
  import torch.nn.functional
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import GridSearchCV, StratifiedKFold
  from sklearn.svm import LinearSVC
label_desc: Use image node_id 1 for calling a global function with example usage: linearsvc(embeds, labels) and returns: np,  np