code: def _get_cloud_mask_variable_info(
      var_name: str, resolution: int
  ) -> dict:
      num_bytes = 6
      shape = _shape_for_resolution(resolution)
      data = np.zeros((num_bytes, shape[0], shape[1]), dtype=np.int8)
      byte_dim_name = "Byte_Segment:mod35"
      row_dim_name = "Cell_Along_Swath_1km:mod35"
      col_dim_name = "Cell_Across_Swath_1km:mod35"
      return {
          var_name: {
              "data": data,
              "type": SDC.INT8,
              "fill_value": 0,
              "attrs": {
                  # dim_labels are just unique dimension names, may not match exactly with real world files
                  "dim_labels": [
                      byte_dim_name,
                      row_dim_name,
                      col_dim_name,
                  ],
                  "valid_range": (0, -1),
                  "scale_factor": 1.0,
                  "add_offset": 0.0,
              },
          },
          "Quality_Assurance": {
              "data": np.ones((shape[0], shape[1], 10), dtype=np.int8),
              "type": SDC.INT8,
              "fill_value": 0,
              "attrs": {
                  # dim_labels are just unique dimension names, may not match exactly with real world files
                  "dim_labels": [
                      row_dim_name,
                      col_dim_name,
                      "Quality_Dimension:mod35",
                  ],
                  "valid_range": (0, -1),
                  "scale_factor": 2.0,
                  "add_offset": -0.5,
              },
          },
      }
  
imports: from __future__ import annotations
  from datetime import datetime, timedelta
  from typing import Optional
  import numpy
  import pytest
  from pyhdf.SD import SD, SDC
label_desc: Use image node_id 27 for calling a global function with example usage: _get_cloud_mask_variable_info(var_name, resolution) and returns: dict