code: def _create_veg_index_variables() -> dict[str, xr.DataArray]:
      dim_y_750 = "Along_Track_750m"
      dim_x_750 = "Along_Scan_750m"
      m_dims = (dim_y_750, dim_x_750)
      dim_y_375 = "Along_Track_375m"
      dim_x_375 = "Along_Scan_375m"
      i_dims = (dim_y_375, dim_x_375)
  
      vi_data = np.zeros((I_ROWS, I_COLS), dtype=np.float32)
      vi_data[0, :7] = [-2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5]
      data_arrs = {
          "NDVI": xr.DataArray(
              vi_data, dims=i_dims, attrs={"units": "unitless"}
          ),
          "EVI": xr.DataArray(
              vi_data, dims=i_dims, attrs={"units": "unitless"}
          ),
      }
      data_arrs["NDVI"].encoding["dtype"] = np.float32
      data_arrs["EVI"].encoding["dtype"] = np.float32
  
      # Quality Flags are from the Surface Reflectance data, but only used for VI products in the reader
      for qf_num in range(1, 8):
          qf_name = f"QF{qf_num} Surface Reflectance"
          qf_data = np.zeros((M_ROWS, M_COLS), dtype=np.uint8)
          bad_qf_start = 4  # 0.5x the last test pixel set in "vi_data" above (I-band versus M-band index)
          if qf_num == 1:
              qf_data[
                  :, :
              ] |= 0b00000010  # medium cloud mask quality everywhere
              qf_data[0, bad_qf_start] |= 0b11000000  # sun glint
              qf_data[0, bad_qf_start + 1] |= 0b00001100  # cloudy
              qf_data[
                  0, bad_qf_start + 2
              ] = 0b00000001  # low cloud mask quality
          elif qf_num == 2:
              qf_data[:, :] |= 0b00000011  # desert everywhere
              qf_data[0, bad_qf_start + 3] |= 0b00100000  # snow or ice
              qf_data[0, bad_qf_start + 4] |= 0b00001000  # cloud shadow
              qf_data[0, bad_qf_start + 5] = 0b00000001  # deep ocean
          elif qf_num == 7:
              qf_data[0, bad_qf_start + 6] |= 0b00001100  # high aerosol
              qf_data[
                  0, bad_qf_start + 7
              ] |= 0b00000010  # adjacent to cloud
  
          data_arr = xr.DataArray(
              qf_data,
              dims=m_dims,
              attrs={"flag_meanings": QF1_FLAG_MEANINGS},
          )
          data_arr.encoding["dtype"] = np.uint8
          data_arrs[qf_name] = data_arr
      return data_arrs
  
imports: from __future__ import annotations
  import shutil
  from datetime import datetime, timedelta
  from pathlib import Path
  from typing import Iterable
  import dask
  import dask.array
  import numpy
  import numpy.typing
  import pytest
  import xarray
  from pyresample import SwathDefinition
  from pytest import TempPathFactory
  from pytest_lazyfixture import lazy_fixture
label_desc: Use image node_id 9 for calling a global function with example usage: _create_veg_index_variables() and returns: data_arrs