code: def process_climis_crop_production_data(data_dir: str):
      
  
      climis_crop_production_csvs = glob(
          "{data_dir}/Climis South Sudan Crop Production Data/"
          "Crops_EstimatedProductionConsumptionBalance*.csv"
      )
      state_county_df = pd.read_csv(
          f"{data_dir}/ipc_data.csv", skipinitialspace=True
      )
      combined_records = []
  
      for f in climis_crop_production_csvs:
          year = int(f.split("/")[-1].split("_")[2].split(".")[0])
          df = pd.read_csv(f).dropna()
          for i, r in df.iterrows():
              record = {
                  "Year": year,
                  "Month": None,
                  "Source": "CliMIS",
                  "Country": "South Sudan",
              }
              region = r["State/County"].strip()
  
              if region.lower() in state_county_df["State"].str.lower().values:
                  record["State"] = region
                  record["County"] = None
              else:
                  potential_states = state_county_df.loc[
                      state_county_df["County"] == region
                  ]["State"]
                  record["State"] = (
                      potential_states.iloc[0]
                      if len(potential_states) != 0
                      else None
                  )
                  record["County"] = region
  
              for field in r.index:
                  if field != "State/County":
                      if "Net Cereal production" in field:
                          record["Variable"] = "Net Cereal Production"
                          record["Value"] = r[field]
                      if field.split()[-1].startswith("("):
                          record["Unit"] = field.split()[-1][1:-1].lower()
                      else:
                          record["Unit"] = None
  
              combined_records.append(record)
  
      df = pd.DataFrame(combined_records)
      return df
docs: Process CliMIS crop production data