input: Write a python function to train a decision tree classifier on the digits dataset and return the predicted class for the first sample. Include assert statements to verify the output.
  ```python
  def train_and_predict():
      import matplotlib.pyplot as plt
      import numpy as np
      from sklearn.datasets import load_digits
      from sklearn import tree
  
      digits = load_digits()
      clf = tree.DecisionTreeClassifier()
      clf.fit(digits.data, digits.target)
      predicted_classes = clf.predict(digits.data)
      return predicted_classes[0]
  
  assert train_and_predict() == 0  # Adjust the expected value based on actual data
output: def train_and_predict():
      import matplotlib.pyplot as plt
      import numpy as np
      from sklearn.datasets import load_digits
      from sklearn import tree
  
      digits = load_digits()
      clf = tree.DecisionTreeClassifier()
      clf.fit(digits.data, digits.target)
      predicted_classes = clf.predict(digits.data)
      return predicted_classes[0]
  
  assert train_and_predict() == 0  # Adjust the expected value based on actual data