code: def test_force_col_types():
      h2oOriginalTypes = {
          "C1": "real",
          "C2": "int",
          "C3": "int",
          "C4": "int",
          "C5": "int",
          "C6": "string",
          "C7": "real",
          "C8": "string",
          "C9": "real",
          "C10": "real",
          "C11": "enum",
          "C12": "int",
          "C13": "int",
          "C14": "int",
          "C15": "int",
          "C16": "enum",
          "C17": "real",
          "C18": "real",
          "C19": "enum",
          "C20": "enum",
          "C21": "enum",
          "C22": "real",
          "C23": "int",
          "C24": "int",
          "C25": "enum",
          "C26": "enum",
          "C27": "string",
          "C28": "int",
          "C29": "int",
          "C30": "int",
          "C31": "int",
          "C32": "int",
          "C33": "int",
          "C34": "int",
          "C35": "enum",
          "C36": "int",
          "C37": "string",
          "C38": "int",
          "C39": "string",
          "C40": "int",
          "C41": "string",
          "C42": "string",
          "C43": "real",
          "C44": "int",
          "C45": "string",
          "C46": "int",
          "C47": "real",
          "C48": "real",
          "C49": "int",
          "C50": "int",
      }
      # import file
      h2oData = h2o.import_file(
          pyunit_utils.locate("smalldata/parser/synthetic_dataset.csv")
      )  # no col_types specification
      h2oTypes = h2oData.types
      pyunit_utils.equal_two_dicts_string(h2oOriginalTypes, h2oTypes)
  
      h2oTypes[
          "C16"
      ] = "int"  # these will happen without force_col_type
      h2oTypes["C11"] = "int"
      h2oTypes["C4"] = "enum"
      h2oTypes["C21"] = "int"
      h2oData2 = h2o.import_file(
          pyunit_utils.locate("smalldata/parser/synthetic_dataset.csv"),
          col_types=h2oTypes,
      )
      h2o2Types = h2oData2.types
      pyunit_utils.equal_two_dicts_string(h2oTypes, h2o2Types)
  
      h2oTypes = (
          h2oData.types
      )  # these changes needs force_col_types = True
      h2oTypes["C2"] = "real"
      h2oTypes["C49"] = "real"
      h2oTypes["C48"] = "int"
      h2oData3 = h2o.import_file(
          pyunit_utils.locate("smalldata/parser/synthetic_dataset.csv"),
          col_types=h2oTypes,
      )
      h2o3Types = h2oData3.types
      pyunit_utils.equal_two_dicts_string(h2oOriginalTypes, h2o3Types)
  
      # set force_col_types=True to Ensure changes here.
      h2oTypes[
          "C21"
      ] = "real"  # this one does not need force_col_types = True
      h2oData4 = h2o.import_file(
          pyunit_utils.locate("smalldata/parser/synthetic_dataset.csv"),
          col_types=h2oTypes,
          force_col_types=True,
      )
      h2o4Types = h2oData4.types
      if (
          h2oData4["C21"].nacnt()[0] == h2oData4.nrow
      ):  # column of NAs are treated as integer
          h2oTypes["C21"] = h2o4Types["C21"]
      pyunit_utils.equal_two_dicts_string(h2oTypes, h2o4Types)
      pyunit_utils.compare_frames_local(
          h2oData["C2"], h2oData4["C2"], prob=1
      )  # change from int column to real columns should be fine
      pyunit_utils.compare_frames_local(
          h2oData2["C21"], h2oData4["C21"], prob=1
      )  # cat to int and cat to real should provide same answer
      # change from real to int will generate columns with different values due to rounding
      pyunit_utils.compare_frames_local(
          h2oData3["C48"], h2oData4["C48"], prob=1, tol=0.5
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_force_col_types() without return types