input: 
  from sklearn.svm import SVC
  import numpy as np
  from sklearn.metrics import accuracy_score
  
  def evaluate_model_accuracy(X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray) -> float:
      """ Evaluate the accuracy of a Support Vector Classifier (SVC) model trained on given data.
      >>> from sklearn.datasets import make_classification
      >>> X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42)
      >>> from sklearn.model_selection import train_test_split
      >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      0.896969696969697
      """
      
      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
  
output:     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