code: def gpu_load_performance_csv(performance_path, **kwargs):
      """Loads performance data
  
      Returns
      -------
      GPU DataFrame
      """
      chronometer = Chronometer.makeStarted()
  
      cols = [
          "loan_id",
          "monthly_reporting_period",
          "servicer",
          "interest_rate",
          "current_actual_upb",
          "loan_age",
          "remaining_months_to_legal_maturity",
          "adj_remaining_months_to_maturity",
          "maturity_date",
          "msa",
          "current_loan_delinquency_status",
          "mod_flag",
          "zero_balance_code",
          "zero_balance_effective_date",
          "last_paid_installment_date",
          "foreclosed_after",
          "disposition_date",
          "foreclosure_costs",
          "prop_preservation_and_repair_costs",
          "asset_recovery_costs",
          "misc_holding_expenses",
          "holding_taxes",
          "net_sale_proceeds",
          "credit_enhancement_proceeds",
          "repurchase_make_whole_proceeds",
          "other_foreclosure_proceeds",
          "non_interest_bearing_upb",
          "principal_forgiveness_upb",
          "repurchase_make_whole_proceeds_flag",
          "foreclosure_principal_write_off_amount",
          "servicing_activity_indicator",
      ]
  
      dtypes = OrderedDict(
          [
              ("loan_id", "int64"),
              ("monthly_reporting_period", "date"),
              ("servicer", "category"),
              ("interest_rate", "float64"),
              ("current_actual_upb", "float64"),
              ("loan_age", "float64"),
              ("remaining_months_to_legal_maturity", "float64"),
              ("adj_remaining_months_to_maturity", "float64"),
              ("maturity_date", "date"),
              ("msa", "float64"),
              ("current_loan_delinquency_status", "int32"),
              ("mod_flag", "category"),
              ("zero_balance_code", "category"),
              ("zero_balance_effective_date", "date"),
              ("last_paid_installment_date", "date"),
              ("foreclosed_after", "date"),
              ("disposition_date", "date"),
              ("foreclosure_costs", "float64"),
              ("prop_preservation_and_repair_costs", "float64"),
              ("asset_recovery_costs", "float64"),
              ("misc_holding_expenses", "float64"),
              ("holding_taxes", "float64"),
              ("net_sale_proceeds", "float64"),
              ("credit_enhancement_proceeds", "float64"),
              ("repurchase_make_whole_proceeds", "float64"),
              ("other_foreclosure_proceeds", "float64"),
              ("non_interest_bearing_upb", "float64"),
              ("principal_forgiveness_upb", "float64"),
              ("repurchase_make_whole_proceeds_flag", "category"),
              ("foreclosure_principal_write_off_amount", "float64"),
              ("servicing_activity_indicator", "category"),
          ]
      )
      print(performance_path)
      performance_table = pyblazing.create_table(
          table_name="perf",
          type=get_type_schema(performance_path),
          path=performance_path,
          delimiter="|",
          names=cols,
          dtypes=get_dtype_values(dtypes),
          skip_rows=1,
      )
      Chronometer.show(chronometer, "Read Performance CSV")
      return performance_table
  
imports: import time
  from collections import OrderedDict
  import numpy
  import pandas
  import pyblazing
  from pyblazing import DriverType, FileSystemType, SchemaFrom, get_dtype_values
  from sklearn.model_selection import train_test_split
  import xgboost
  from Configuration import Settings
  from DemoTest.chronometer import Chronometer
  from Utils import Execution
label_desc: Use image node_id 8 for calling a global function with example usage: gpu_load_performance_csv(performance_path) and returns: performance_table