code: def svc_classify(x, y, search):
      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]
  
          if search:
              params = {"C": [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
              classifier = GridSearchCV(
                  SVC(), params, cv=5, scoring="accuracy", verbose=0
              )
          else:
              classifier = SVC(C=10)
          classifier.fit(x_train, y_train)
          accuracies.append(
              accuracy_score(y_test, classifier.predict(x_test))
          )
      return np.mean(accuracies)
  
file_path: dgl/examples/pytorch/infograph/evaluate_embedding.py
imports: import numpy
  import torch
  import torch.nn
  from sklearn import preprocessing
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import GridSearchCV, StratifiedKFold
  from sklearn.svm import SVC
label_desc: Use image node_id 2 for calling a global function with example usage: svc_classify(x, y, search) and returns: np