input: Write a Python function to train a logistic regression model and return the accuracy score given a CSV file path.
  assert round(train_logistic_regression("cancer_data.csv"), 2) == 0.95
  assert round(train_logistic_regression("another_cancer_data.csv"), 2) == 0.87
output: def train_logistic_regression(csv_file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      # Read the CSV file
      data = pd.read_csv(csv_file_path)
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # 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)
  
      # Create and train the logistic regression model
      model = LogisticRegression(max_iter=10000)
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate and return the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy