code: import os
  import numpy as np
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  from multiprocessing import Process
  
  
  def startTensorboard(logdir):
      # Start tensorboard with system call
      os.system("tensorboard --logdir {}".format(logdir))
  
  
  def fitModel():
      # Create your model
      model = Sequential()
      model.add(Dense(32, activation='relu', input_dim=100))
      model.add(Dense(1, activation='sigmoid'))
      model.compile(optimizer='rmsprop',
                    loss='binary_crossentropy',
                    metrics=['accuracy'])
  
      # Some mock training data
      data = np.random.random((1000, 100))
      labels = np.random.randint(2, size=(1000, 1))
  
      # Run the fit function
      model.fit(data, labels, epochs=100, batch_size=32)
  
  
  if __name__ == '__main__':
      # Run both processes simultaneously
      Process(target=startTensorboard, args=("logs",)).start()
      Process(target=fitModel).start()
apis: numpy.random.random tensorflow.keras.models.Sequential numpy.random.randint tensorflow.keras.layers.Dense