input: Write a function to evaluate the accuracy of a machine-learning model using a Support Vector Classifier with polynomial kernel. The function should take the training and testing data as input, fit the model, make predictions on the testing data, and return the accuracy score.
  
  from sklearn.svm import SVC
  import numpy as np
  from sklearn.metrics import accuracy_score
  
  def evaluate_model(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)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy
  
  # Test cases
  X_train = np.array([[1, 2], [2, 3], [3, 4]])
  y_train = np.array([0, 1, 1])
  X_test = np.array([[4, 5], [5, 6]])
  y_test = np.array([1, 1])
  
  assert evaluate_model(X_train, y_train, X_test, y_test) == 1.0
  
  X_train = np.array([[1, 2], [2, 3], [3, 4]])
  y_train = np.array([0, 1, 0])
  X_test = np.array([[4, 5], [5, 6]])
  y_test = np.array([1, 0])
  
  assert evaluate_model(X_train, y_train, X_test, y_test) == 0.5
output: def evaluate_model(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