code: def pubdev_1953():
      # small_test = [pyunit_utils.locate("bigdata/laptop/citibike-nyc/2013-10.csv")]
      # data = h2o.import_file(path=small_test)
      # startime = data["starttime"]
      # secsPerDay=1000*60*60*24
      # data["Days"] = (startime/secsPerDay).floor()
      # grouped = data.group_by(["Days","start station name"])
      # bpd = grouped.count(name="bikes").get_frame()
      # secs = bpd["Days"]*secsPerDay
      # bpd["Month"]     = secs.month().asfactor()
      # bpd["DayOfWeek"] = secs.dayOfWeek()
      # wthr1 = h2o.import_file(path=[pyunit_utils.locate("bigdata/laptop/citibike-nyc/31081_New_York_City__Hourly_2013.csv"), pyunit_utils.locate("bigdata/laptop/citibike-nyc/31081_New_York_City__Hourly_2014.csv")])
      # wthr2 = wthr1[["Year Local","Month Local","Day Local","Hour Local","Dew Point (C)","Humidity Fraction","Precipitation One Hour (mm)","Temperature (C)","Weather Code 1/ Description"]]
      # wthr2.set_name(wthr2.index("Precipitation One Hour (mm)"), "Rain (mm)")
      # wthr2.set_name(wthr2.index("Weather Code 1/ Description"), "WC1")
      # wthr3 = wthr2[ wthr2["Hour Local"]==12 ]
      # wthr3["msec"] = h2o.H2OFrame.moment(year=wthr3["Year Local"], month=wthr3["Month Local"], day=wthr3["Day Local"], hour=wthr3["Hour Local"])
      # secsPerDay=1000*60*60*24
      # wthr3["Days"] = (wthr3["msec"]/secsPerDay).floor()
      # wthr4 = wthr3.drop("Year Local").drop("Month Local").drop("Day Local").drop("Hour Local").drop("msec")
      # rain = wthr4["Rain (mm)"]
      # rain[ rain.isna() ] = 0
      # bpd_with_weather = bpd.merge(wthr4,allLeft=True,allRite=False)
      # r = bpd_with_weather['Days'].runif(seed=356964763)
      # train = bpd_with_weather[  r  < 0.6]
      # test  = bpd_with_weather[(0.6 <= r) & (r < 0.9)]
  
      predictors = [
          "DayOfWeek",
          "WC1",
          "start station name",
          "Temperature (C)",
          "Days",
          "Month",
          "Humidity Fraction",
          "Rain (mm)",
          "Dew Point (C)",
      ]
  
      train = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/citibike_small_train.csv"
          )
      )
      test = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/citibike_small_test.csv"
          )
      )
  
      glm0 = h2o.glm(
          x=train[predictors],
          y=train["bikes"],
          validation_x=test[predictors],
          validation_y=test["bikes"],
          family="poisson",
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: pubdev_1953() without return types