input: Write a Python function to train a logistic regression model on a text dataset using TF-IDF vectorization.
  assert train_logistic_regression_model('dataset.csv') is not None
output: def train_logistic_regression_model(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.pipeline import Pipeline
      from sklearn.metrics import accuracy_score
      
      # Load the dataset
      df = pd.read_csv(file_path)
      
      # Assuming the dataset has 'text' and 'label' columns
      X = df['text']
      y = df['label']
      
      # Split the dataset 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 a pipeline with TF-IDF vectorization and logistic regression
      pipeline = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('logreg', LogisticRegression())
      ])
      
      # Train the model
      pipeline.fit(X_train, y_train)
      
      # Make predictions and evaluate the model
      y_pred = pipeline.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      print(f"Model accuracy: {accuracy}")
      
      return pipeline