input: Write a function that generates a Python code for a Machine Learning model capable of recognizing handwritten digits from 0 to 9. The function should not take any input and should return the complete code as a multi-line string. It should include importing necessary libraries, loading the MNIST dataset, defining and compiling the neural network model, and fitting the model on the training data.
  assert generate_code_for_digit_recognition()=='''
  # Importing necessary libraries
  
  import numpy as np
  import pandas as pd
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.layers import Dense, Input, Dropout
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import to_categorical
  
  # Loading data
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # Defining the neural network model
  
  # Input layer
  inputs = Input(shape=(28,28,))
  
  # Hidden layer
  h1 = Dense(64, activation='relu')(inputs)
  h2 = Dropout(0.5)(h1)
  
  # Output layer
  outputs = Dense(10, activation='softmax')(h2)
  
  # Compiling the model
  model = Model(inputs=inputs, outputs=outputs)
  model.compile(loss='categorical_crossentropy',
   optimizer='adam',
   metrics=['accuracy'])
  
  # Data pre-processing
  
  # Reshape inputs from (60000, 28, 28) to (60000, 784)
  X_train = X_train.reshape(60000, 784)
  X_test = X_test.reshape(10000, 784)
  
  # Normalize pixel values
  X_train = X_train.astype('float32')
  X_train /= 255
  X_test = X_test.astype('float32')
  X_test /= 255
  
  # One-hot encode labels
  y_train = to_categorical(y_train, num_classes=10)
  y_test = to_categorical(y_test, num_classes=10)
  
  # Fitting the model
  model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)
  '''
output: def generate_code_for_digit_recognition():
      return '''
  # Importing necessary libraries
  import numpy as np
  import pandas as pd
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.layers import Dense, Input, Dropout
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import to_categorical
  
  # Loading data
  (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
  # Defining the neural network model
  
  # Input layer
  inputs = Input(shape=(28,28,))
  
  # Hidden layer
  h1 = Dense(64, activation='relu')(inputs)
  h2 = Dropout(0.5)(h1)
  
  # Output layer
  outputs = Dense(10, activation='softmax')(h2)
  
  # Compiling the model
  model = Model(inputs=inputs, outputs=outputs)
  model.compile(loss='categorical_crossentropy',
   optimizer='adam',
   metrics=['accuracy'])
  
  # Data pre-processing
  
  # Reshape inputs from (60000, 28, 28) to (60000, 784)
  X_train = X_train.reshape(60000, 784)
  X_test = X_test.reshape(10000, 784)
  
  # Normalize pixel values
  X_train = X_train.astype('float32')
  X_train /= 255
  X_test = X_test.astype('float32')
  X_test /= 255
  
  # One-hot encode labels
  y_train = to_categorical(y_train, num_classes=10)
  y_test = to_categorical(y_test, num_classes=10)
  
  # Fitting the model
  model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)
  '''