input: def recognize_handwritten_digits() -> str:
      """ Generates a Machine Learning model in Python which can recognize handwritten digits from 0 to 9.
      The model uses a neural network with an input layer of shape (28,28), a hidden layer with 64 neurons,
      and an output layer with 10 neurons corresponding to digits 0 to 9. The output is the summary of the trained model.
      
      >>> recognize_handwritten_digits()
      'Model: "functional_1"\\n_________________________________________________________________\\nLayer (type)                 Output Shape              Param #  \\n=================================================================\\ninput_1 (InputLayer)         [(None, 28, 28)]          0        \\n_________________________________________________________________\\ndense (Dense)                (None, 64)                50240    \\n_________________________________________________________________\\ndropout (Dropout)            (None, 64)                0        \\n_________________________________________________________________\\ndense_1 (Dense)              (None, 10)                650      \\n=================================================================\\nTotal params: 50,890\\nTrainable params: 50,890\\nNon-trainable params: 0\\n_________________________________________________________________\\n'
      """
output:     import tensorflow as tf
      from tensorflow.keras.models import Model
      from tensorflow.keras.layers import Input, Dense, Dropout
  
      input_layer = Input(shape=(28, 28))
      flatten_layer = tf.keras.layers.Flatten()(input_layer)
      hidden_layer = Dense(64, activation='relu')(flatten_layer)
      dropout_layer = Dropout(0.2)(hidden_layer)
      output_layer = Dense(10, activation='softmax')(dropout_layer)
  
      model = Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      return model.summary()