input: Write a function to evaluate the accuracy of a machine-learning model using a Support Vector Classifier with a polynomial kernel of degree 2 and gamma set to 0.05. The function should take training and testing data as input, fit the model, predict the test data, and then calculate and return the accuracy.
  
  from sklearn.svm import SVC
  import numpy as np
  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
  
  # Testing the function with sample data
  X_train = np.array([[0, 0], [1, 1]])
  y_train = np.array([0, 1])
  X_test = np.array([[2., 2.], [3., 3.]])
  y_test = np.array([1, 1])
  
  assert evaluate_model_accuracy(X_train, y_train, X_test, y_test) == 0.5
  
  X_train = np.array([[0, 0], [1, 1], [2, 2]])
  y_train = np.array([0, 1, 1])
  X_test = np.array([[3., 3.], [4., 4.]])
  y_test = np.array([1, 1])
  
  assert evaluate_model_accuracy(X_train, y_train, X_test, y_test) == 1.0
output: 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