input: Write a function to implement a machine learning algorithm in Python that identifies apples from bananas using a Support Vector Machine (SVM) with a linear kernel. The function should load the training data, initialize the classifier, fit the data, and then test the classifier on a given test dataset. The function should return the predictions for the test dataset.
  
  assert identify_fruits(X, y)==np.array([0, 1])
output: def identify_fruits(X, y):
      from sklearn.svm import SVC
      from sklearn.model_selection import train_test_split
      import numpy as np
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
      clf = SVC(kernel='linear')
      clf.fit(X_train, y_train)
      predictions = clf.predict(X_test)
      return predictions