code: def generate_mock_data(rows=100):
      return dict(
          columns=[
              dict(id="rows", type="numeric", editable=False),
              dict(
                  id="ccc",
                  name=["City", "Canada", "Toronto"],
                  type="numeric",
              ),
              dict(
                  id="ddd",
                  name=["City", "Canada", "Montréal"],
                  type="numeric",
              ),
              dict(
                  id="eee",
                  name=["City", "America", "New York City"],
                  type="numeric",
              ),
              dict(
                  id="fff",
                  name=["City", "America", "Boston"],
                  type="numeric",
              ),
              dict(
                  id="ggg",
                  name=["City", "France", "Paris"],
                  type="numeric",
              ),
              dict(
                  id="bbb",
                  name=["", "Weather", "Climate"],
                  type="text",
                  presentation="dropdown",
              ),
              dict(
                  id="bbb-readonly",
                  name=["", "Weather", "Climate-RO"],
                  type="text",
                  editable=False,
                  presentation="dropdown",
              ),
              dict(
                  id="aaa",
                  name=["", "Weather", "Temperature"],
                  type="numeric",
              ),
              dict(
                  id="aaa-readonly",
                  name=["", "Weather", "Temperature-RO"],
                  type="numeric",
                  editable=False,
              ),
          ],
          data=[
              {
                  "rows": i,
                  "ccc": i,
                  "ddd": i,
                  "eee": i,
                  "fff": i + 1,
                  "ggg": i * 10,
                  "bbb": ["Humid", "Wet", "Snowy", "Tropical Beaches"][
                      i % 4
                  ],
                  "bbb-readonly": [
                      "Humid",
                      "Wet",
                      "Snowy",
                      "Tropical Beaches",
                  ][i % 4],
                  "aaa": i + 1,
                  "aaa-readonly": i + 1,
              }
              for i in range(rows)
          ],
          dropdown={
              "bbb": dict(
                  clearable=True,
                  options=[
                      dict(label="label {}".format(i), value=i)
                      for i in [
                          "Humid",
                          "Wet",
                          "Snowy",
                          "Tropical Beaches",
                      ]
                  ],
              ),
              "bbb-readonly": dict(
                  clearable=True,
                  options=[
                      dict(label="label {}".format(i), value=i)
                      for i in [
                          "Humid",
                          "Wet",
                          "Snowy",
                          "Tropical Beaches",
                      ]
                  ],
              ),
          },
          style_cell_conditional=[
              {
                  "if": dict(
                      column_id="rows",
                  ),
                  "maxWidth": 60,
                  "minWidth": 60,
                  "width": 60,
              },
              {
                  "if": dict(
                      column_id="bbb",
                  ),
                  "maxWidth": 200,
                  "minWidth": 200,
                  "width": 200,
              },
              {
                  "if": dict(
                      column_id="bbb-readonly",
                  ),
                  "maxWidth": 200,
                  "minWidth": 200,
                  "width": 200,
              },
          ],
      )
  
imports: from datetime import date, timedelta
label_desc: Use image node_id 3 for calling a global function with example usage: generate_mock_data(rows) and returns: dict