input: Write a python function to train a logistic regression model on a given dataset and return the accuracy score.
  assert train_logistic_regression('wine_data.csv') > 0
  assert isinstance(train_logistic_regression('wine_data.csv'), float)
  assert 0 <= train_logistic_regression('wine_data.csv') <= 1
output: def train_logistic_regression(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
  
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assuming the target column is named 'target'
      X = data.drop(columns='target')
      y = data['target']
      
      # 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)
      
      # Initialize and train the logistic regression model
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = model.predict(X_test)
      
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy