input: def Recognize_Handwritten_Digits() -> None:
      """This function generates code for a Machine Learning model in Python which can recognize handwritten digits from 0 to 9 using TensorFlow and Keras. The model consists of an input layer, a hidden layer with dropout, and an output layer. The function also includes data pre-processing steps such as reshaping, normalizing pixel values, and one-hot encoding the labels. The model is then compiled and trained on the MNIST dataset.
      
      Example:
      Recognize_Handwritten_Digits()
      """
output:     import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      # Load and preprocess the MNIST dataset
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      # Reshape the data to fit the model (28x28 pixels)
      x_train = x_train.reshape((x_train.shape[0], 28, 28, 1))
      x_test = x_test.reshape((x_test.shape[0], 28, 28, 1))
  
      # Normalize pixel values to be between 0 and 1
      x_train = x_train.astype('float32') / 255
      x_test = x_test.astype('float32') / 255
  
      # One-hot encode the labels
      y_train = to_categorical(y_train)
      y_test = to_categorical(y_test)
  
      # Build the model
      model = models.Sequential()
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.Flatten())
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dropout(0.5))
      model.add(layers.Dense(10, activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      # Train the model
      model.fit(x_train, y_train, epochs=5, batch_size=64, validation_data=(x_test, y_test))
  
      # Evaluate the model
      test_loss, test_acc = model.evaluate(x_test, y_test)
      print(f'Test accuracy: {test_acc}')