old_contents: # This script should run without errors whenever we update the
  # kaggle/python container. It checks that all our most popular packages can
  # be loaded and used without errors.
  
  import numpy as np
  print("Numpy imported ok")
  print("Your lucky number is: " + str(np.random.randint(100)))
  
  import pandas as pd
  print("Pandas imported ok")
  
  from sklearn import datasets
  print("sklearn imported ok")
  iris = datasets.load_iris()
  X, y = iris.data, iris.target
  
  from sklearn.ensemble import RandomForestClassifier
  rf1 = RandomForestClassifier()
  rf1.fit(X,y)
  print("sklearn RandomForestClassifier: ok")
  
  from xgboost import XGBClassifier
  xgb1 = XGBClassifier(n_estimators=3)
  xgb1.fit(X[0:70],y[0:70])
  print("xgboost XGBClassifier: ok")
  
  import matplotlib.pyplot as plt
  plt.plot(np.linspace(0,1,50), np.random.rand(50))
  plt.savefig("plot1.png")
  print("matplotlib.pyplot ok")
  
  from mpl_toolkits.basemap import Basemap
  print("Basemap ok")
  
  import plotly.plotly as py
  import plotly.graph_objs as go
  print("plotly ok")
  
  from ggplot import *
  print("ggplot ok")
  
  import theano
  print("Theano ok")
  
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation, Flatten
  from keras.layers.convolutional import Convolution2D, MaxPooling2D
  from keras.optimizers import SGD
  print("keras ok")
  
  import nltk
  from nltk.stem import WordNetLemmatizer
  print("nltk ok")
  
  import tensorflow as tf
  hello = tf.constant('TensorFlow ok')
  sess = tf.Session()
  print(sess.run(hello))
  
  import cv2
  img = cv2.imread('plot1.png',0)
  print("OpenCV ok")
  
  import essentia
  print("Essentia ok")
  
new_contents: # This script should run without errors whenever we update the
  # kaggle/python container. It checks that all our most popular packages can
  # be loaded and used without errors.
  
  import numpy as np
  print("Numpy imported ok")
  print("Your lucky number is: " + str(np.random.randint(100)))
  
  import pandas as pd
  print("Pandas imported ok")
  
  from sklearn import datasets
  print("sklearn imported ok")
  iris = datasets.load_iris()
  X, y = iris.data, iris.target
  
  from sklearn.ensemble import RandomForestClassifier
  rf1 = RandomForestClassifier()
  rf1.fit(X,y)
  print("sklearn RandomForestClassifier: ok")
  
  from xgboost import XGBClassifier
  xgb1 = XGBClassifier(n_estimators=3)
  xgb1.fit(X[0:70],y[0:70])
  print("xgboost XGBClassifier: ok")
  
  import matplotlib.pyplot as plt
  plt.plot(np.linspace(0,1,50), np.random.rand(50))
  plt.savefig("plot1.png")
  print("matplotlib.pyplot ok")
  
  from mpl_toolkits.basemap import Basemap
  print("Basemap ok")
  
  import plotly.plotly as py
  import plotly.graph_objs as go
  print("plotly ok")
  
  from ggplot import *
  print("ggplot ok")
  
  import theano
  print("Theano ok")
  
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation, Flatten
  from keras.layers.convolutional import Convolution2D, MaxPooling2D
  from keras.optimizers import SGD
  print("keras ok")
  
  import nltk
  from nltk.stem import WordNetLemmatizer
  print("nltk ok")
  
  import tensorflow as tf
  hello = tf.constant('TensorFlow ok')
  sess = tf.Session()
  print(sess.run(hello))
  
  import cv2
  img = cv2.imread('plot1.png',0)
  print("OpenCV ok")
  
subject: Revert Essentia from build test