code: def user():
      a = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )[0:4]
      a.head()
  
      print(a[0].names)  # Column header
      print(a[2, 0])  # column 0, row 2 value
      print(a[2, "sepal_len"])  # Column 0, row 2 value
      (a[0] + 2).show()  # Add 2 to every element; broadcast a constant
      (a[0] + a[1]).show()  # Add 2 columns; broadcast parallel add
      sum(a).show()
      print(a["sepal_len"].mean())
  
      print()
      print("Rows 50 through 77 in the `sepal_len` column")
      a[
          50:78, "sepal_len"
      ].show()  # print out rows 50 thru 77 inclusive
      print()
  
      a["sepal_len"].show()
  
      print(a[50:78, ["sepal_len", "sepal_wid"]].show())
  
      a.show()
  
      print("The column means: ")
      print(a.mean())
      print()
  
      try:
          print(a["Sepal_len"].dim)  # Error, misspelt column name
      except Exception:
          pass  # Expected error
  
      b = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )[0:4]
      c = a + b
      d = c + c + sum(a)
      e = c + a + 1
      e.show()
      # Note that "d=c+..." keeps the internal C expressions alive, until "d" goes
      # out of scope even as we nuke "c"
      c.show()
      c = None
      # Internal "ExprNode(c=a+b)" not dead!
  
      print(1 + (a[0] + b[1]).mean())
  
      import collections
  
      c = h2o.H2OFrame(
          collections.OrderedDict({"A": [1, 2, 3], "B": [4, 5, 6]})
      )
      c.show()
  
      c.describe()
      c.head()
  
      c[0].show()
      print(c[1, 0])
      c[0:2, 0].show()
  
      sliced = a[0:51, 0]
      sliced.show()
  
label_desc: Use image node_id 1 for calling a global function with example usage: user() without return types