code: def glrm_subset():
      acs_orig = h2o.upload_file(
          path=pyunit_utils.locate(
              "bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip"
          ),
          col_types=(["enum"] + ["numeric"] * 149),
      )
  
      acs_full = acs_orig.drop("ZCTA5")
      acs_model = H2OGeneralizedLowRankEstimator(
          k=10,
          transform="STANDARDIZE",
          loss="Quadratic",
          regularization_x="Quadratic",
          regularization_y="L1",
          gamma_x=0.25,
          gamma_y=0.5,
          max_iterations=1,
      )
  
      acs_model.train(x=acs_full.names, training_frame=acs_full)
      zcta_arch_x = h2o.get_frame(
          acs_model._model_json["output"]["representation_name"]
      )
      print(zcta_arch_x)
  
      acs_zcta_col = acs_orig["ZCTA5"].asfactor()
  
      idx = (
          (acs_zcta_col == "10065")
          | (  # Manhattan, NY (Upper East Side)\n",
              acs_zcta_col == "11219"
          )
          | (acs_zcta_col == "66753")  # Manhattan, NY (East Harlem)\n",
          | (acs_zcta_col == "84104")  # McCune, KS\n",
          | (acs_zcta_col == "94086")  # Salt Lake City, UT\n",
          | (acs_zcta_col == "95014")  # Sunnyvale, CA\n",
      )  # Cupertino, CA\n",
  
      print(zcta_arch_x[idx, [0, 1]])
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: glrm_subset() without return types