input: Write a function to evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC) with a polynomial kernel. The function should take training features (X_train), training labels (y_train), testing features (X_test), and testing labels (y_test) as inputs, fit the model, make predictions on the test data, and return the accuracy.
  import numpy as np
  from sklearn.svm import SVC
  from sklearn.metrics import accuracy_score
  
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  
  assert evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[2, 2], [3, 3]], [1, 0])==0.5
  assert evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[0, 0], [1, 1]], [0, 1])==1.0
  assert evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[2, 2], [3, 3], [4, 4]], [1, 0, 1])==0.3333333333333333
  assert evaluate_model_accuracy([], [], [], [])==0.0
output: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      if not X_train or not X_test:
          return 0.0
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy