input: Write a python function to train a logistic regression model and evaluate its accuracy on a given dataset.
  assert train_and_evaluate_logistic_regression("data.csv", "target_column") > 0.7
  assert train_and_evaluate_logistic_regression("new_data.csv", "class_label") > 0.65
output: def train_and_evaluate_logistic_regression(file_path, target_column):
      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 dataset
      df = pd.read_csv(file_path)
  
      # Split data into features and target
      X = df.drop(columns=[target_column])
      y = df[target_column]
  
      # Split data into training and test 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 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 accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy