input: Write a function to generate a Machine Learning model in Python that can recognize handwritten digits from 0 to 9 using the MNIST dataset. The function should load the dataset, preprocess the data, define a neural network model, compile it, and fit it to the training data. Return the trained model.
  
  def digit_recognition_model():
      # 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
      inputs = Input(shape=(28,28,))
      h1 = Dense(64, activation='relu')(inputs)
      h2 = Dropout(0.5)(h1)
      outputs = Dense(10, activation='softmax')(h2)
      model = Model(inputs=inputs, outputs=outputs)
      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      # Data pre-processing
      X_train = X_train.reshape(60000, 784)
      X_test = X_test.reshape(10000, 784)
      X_train = X_train.astype('float32') / 255
      X_test = X_test.astype('float32') / 255
      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)
      return model
  
  assert isinstance(digit_recognition_model(), Model) == True
output: def digit_recognition_model():
      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
  
      (X_train, y_train), (X_test, y_test) = mnist.load_data()
  
      inputs = Input(shape=(28,28,))
      h1 = Dense(64, activation='relu')(inputs)
      h2 = Dropout(0.5)(h1)
      outputs = Dense(10, activation='softmax')(h2)
      model = Model(inputs=inputs, outputs=outputs)
      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      X_train = X_train.reshape(60000, 784)
      X_test = X_test.reshape(10000, 784)
      X_train = X_train.astype('float32') / 255
      X_test = X_test.astype('float32') / 255
      y_train = to_categorical(y_train, num_classes=10)
      y_test = to_categorical(y_test, num_classes=10)
  
      model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)
      return model