input: Write a python function to read a CSV file and perform train-test split, feature scaling, and SVM training, then return the accuracy and confusion matrix.
  assert isinstance(run_svm_pipeline('data.csv'), tuple)
  assert len(run_svm_pipeline('data.csv')) == 2
  assert isinstance(run_svm_pipeline('data.csv')[0], float)
  assert isinstance(run_svm_pipeline('data.csv')[1], list)
output: def run_svm_pipeline(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score, confusion_matrix
  
      # Read the CSV file
      data = pd.read_csv(file_path)
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Feature scaling
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the SVM model
      model = SVC()
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      # Calculate confusion matrix
      confusion = confusion_matrix(y_test, y_pred).tolist()
  
      return accuracy, confusion