text: 
  from numpy import loadtxt
  import numpy as np
  
  import tensorflow as tf
  
  
  from tensorflow.keras.layers import Dense, BatchNormalization
  from tensorflow.keras.models import Sequential
  
  ## Explicitly disable GPU to force CPU usage
  import os
  os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
  os.environ['AUTOGRAPH_VERBOSITY'] = '2'
  
  tf.get_logger().setLevel('ERROR')
  
  
  TENSOR_SIZE = 64
  OUTPUT_SIZE = 8
  BATCH_SIZE = 4
  
  num_data = int(TENSOR_SIZE * 10)
  X = np.random.rand(num_data,TENSOR_SIZE)
  y = np.random.rand(num_data,OUTPUT_SIZE)
  
  
  model = Sequential()
  model.add(Dense(TENSOR_SIZE, input_dim = TENSOR_SIZE , activation="tanh"))
  model.add(Dense(32, activation="tanh"))
  model.add(BatchNormalization())
  model.add(Dense(16, activation="tanh"))
  model.add(Dense(OUTPUT_SIZE, input_dim = TENSOR_SIZE , activation="tanh"))
  model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
  
  
  model.fit(X, y, epochs=1, batch_size=BATCH_SIZE)
  
  _, accuracy = model.evaluate(X, y)
  print('Accuracy: %.2f' % (accuracy*100))
  