input: Write a python function to create and evaluate a Machine Learning pipeline using Logistic Regression and Standard Scaler.
  assert test_accuracy(create_pipeline(X_train, X_test, y_train, y_test)) == expected_accuracy
  assert test_accuracy(create_pipeline(X_train_sample, X_test_sample, y_train_sample, y_test_sample)) == expected_accuracy_sample
  assert test_accuracy(create_pipeline(another_X_train, another_X_test, another_y_train, another_y_test)) == expected_accuracy_another
output: def create_pipeline(X_train, X_test, y_train, y_test):
      from sklearn.pipeline import Pipeline
      from sklearn.preprocessing import StandardScaler
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      # Create a pipeline with StandardScaler and LogisticRegression
      pipeline = Pipeline([
          ('scaler', StandardScaler()),
          ('logreg', LogisticRegression())
      ])
  
      # Train the pipeline
      pipeline.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = pipeline.predict(X_test)
  
      # Calculate and return the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy