input: 
  def recognize_handwritten_digits(epochs: int, batch_size: int) -> Model:
      """ Define a neural network model using TensorFlow and Keras that can recognize handwritten
      digits from 0 to 9. The model is trained on the MNIST dataset. The function returns the 
      trained model.
      
      >>> model = recognize_handwritten_digits(epochs=5, batch_size=128)
      >>> test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)
      >>> test_acc > 0.90
      True
      """
      
      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()
  
      # 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
      X_train = X_train.reshape(60000, 784)
      X_test = X_test.reshape(10000, 784)
  
      X_train = X_train.astype('float32')
      X_train /= 255
      X_test = X_test.astype('float32')
      X_test /= 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=epochs, batch_size=batch_size)
      
      return model
  
output:     return model