code: def op_precedence():
      # Connect to a pre-existing cluster
  
      a = [
          [random.uniform(-100, 100) for r in range(10)]
          for c in range(10)
      ]
      b = [
          [random.uniform(-100, 100) for r in range(10)]
          for c in range(10)
      ]
      c = [
          [random.uniform(-100, 100) for r in range(10)]
          for c in range(10)
      ]
  
      A = h2o.H2OFrame(a)
      B = h2o.H2OFrame(b)
      C = h2o.H2OFrame(c)
  
      np_A = np.array(a)
      np_B = np.array(b)
      np_C = np.array(c)
  
      s1 = np_A + np_B * np_C
      s2 = np_A - np_B - np_C
      s3 = np_A**1**2
      s4 = np.logical_and(np_A == np_B, np_C)
      s5 = np_A == np_B + np_C
      s6 = np.logical_and(np.logical_or(np_A, np_B), np_C)
  
      print("Check A + B * C")
      S1 = A + B * C
      pyunit_utils.np_comparison_check(S1, s1, 10)
  
      print("Check A - B - C")
      S2 = A - B - C
      pyunit_utils.np_comparison_check(S2, s2, 10)
  
      print("Check A ^ 2 ^ 3")
      S3 = A**1**2
      pyunit_utils.np_comparison_check(S3, s3, 10)
  
      print("Check A == B & C")
      S4 = A == B & C
      pyunit_utils.np_comparison_check(S4, s4, 10)
  
      print("Check A == B + C")
      S5 = A == B + C
      pyunit_utils.np_comparison_check(S5, s5, 10)
  
      print("Check A | B & C")
      S6 = A | B & C
      pyunit_utils.np_comparison_check(S6, s6, 10)
  
imports: from builtins import zip
  from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  import random
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: op_precedence() without return types