input: Write a function to evaluate the accuracy of a machine-learning model using the accuracy_score function from the sklearn.metrics module. The function should take in the trained model, the training data (X_train and y_train), and the test data (X_test and y_test) as inputs. It should return the accuracy of the model as an output.
  
  
  from sklearn.metrics import accuracy_score
  
  def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy
  
  # Example usage:
  from sklearn.svm import SVC
  X_train, y_train, X_test, y_test = ...
  clf = SVC(kernel='poly', degree=2, gamma=0.05)
  accuracy = evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
  assert accuracy == 0.95  # Example accuracy value
  
  
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test) == 0.95
  assertEqual evalwulate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test), 0.95
  assert accuracy == 0.95
output: def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy