code: def rmsle(expected, predicted):
      log_expected = np.log1p(expected + 1)
      log_predicted = np.log1p(predicted + 1)
      squared_log_error = np.square(log_expected - log_predicted)
      return np.sqrt(np.mean(squared_log_error))
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn import datasets, preprocessing
  from sklearn.model_selection import train_test_split
  from neupy import algorithms, layers
label_desc: Use image node_id 1 for calling a global function with example usage: rmsle(expected, predicted) and returns: np