input: Write a python function to train an SVM classifier for sentiment analysis using TF-IDF vectorization on a given dataset.
  ```python
  import pandas as pd
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.svm import SVC
  
  def train_svm_classifier(dataset):
      df = pd.DataFrame(dataset)
      X = df['text']
      y = df['sentiment']
  
      # Create the vectorizer
      vectorizer = TfidfVectorizer()
  
      # Fit and transform the vectorizer to X
      X_vect = vectorizer.fit_transform(X)
  
      # Initialize the SVM classifier
      clf = SVC()
  
      # Fit the SVM classifier to the data
      clf.fit(X_vect, y)
      return clf, vectorizer
  
  assert isinstance(train_svm_classifier([{'text': 'I love this movie', 'sentiment': 1}, {'text': 'I hate this movie', 'sentiment': 0}]), tuple)
  assert len(train_svm_classifier([{'text': 'I love this movie', 'sentiment': 1}, {'text': 'I hate this movie', 'sentiment': 0}])) == 2
output: def train_svm_classifier(dataset):
      df = pd.DataFrame(dataset)
      X = df['text']
      y = df['sentiment']
  
      # Create the vectorizer
      vectorizer = TfidfVectorizer()
  
      # Fit and transform the vectorizer to X
      X_vect = vectorizer.fit_transform(X)
  
      # Initialize the SVM classifier
      clf = SVC()
  
      # Fit the SVM classifier to the data
      clf.fit(X_vect, y)
      return clf, vectorizer