code: def evaluate_embedding(embeddings, labels, search=True, device="cpu"):
      labels = preprocessing.LabelEncoder().fit_transform(labels)
      x, y = np.array(embeddings), np.array(labels)
  
      logreg_accuracy = logistic_classify(x, y, device)
      print("LogReg", logreg_accuracy)
      svc_accuracy = svc_classify(x, y, search)
      print("svc", svc_accuracy)
  
      return logreg_accuracy, svc_accuracy
  
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 3 for calling a global function with example usage: evaluate_embedding(embeddings, labels, search, device) and returns: logreg_accuracy,  svc_accuracy