code: def sales_projections(employee_data):
      sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy()
      regression_values = np.apply_along_axis(
          lambda row: np.array(
              np.poly1d(np.polyfit([0, 1, 2], row, 2))
          ),
          0,
          sales_data,
      )
      projected_months = np.repeat(
          np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0
      )
      projected_values = np.array(
          [
              month * month * regression[0]
              + month * regression[1]
              + regression[2]
              for month, regression in zip(
                  projected_months, regression_values
              )
          ]
      )
      plt.plot(projected_values.T)
      plt.legend(employee_data["Name"])
      return employee_data, plt.gcf(), regression_values
  
label_desc: Use image node_id 1 for calling a global function with example usage: sales_projections(employee_data) and returns: employee_data,  plt,  regression_values