input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories. The function should load a CSV file containing text data and a corresponding category, vectorize the text using CountVectorizer, split the data into training and testing sets, train a Multinomial Naive Bayes model, and then print a classification report.
  
  assert classify_text_data()=="""
                precision    recall  f1-score   support
  
             1       0.89      0.91      0.90        98
             2       0.87      0.85      0.86        89
             3       0.91      0.92      0.92       110
  
      accuracy                           0.89       297
     macro avg       0.89      0.89      0.89       297
  weighted avg       0.89      0.89      0.89       297
  """
output: def classify_text_data():
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      data = pd.read_csv('text_data.csv')
      X = data['text']
      y = data['category']
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred, output_dict=True)
      return classification_report(y_test, y_pred)