code: def dummy_timeseries(
      length,
      n_series=1,
      comps_target=1,
      comps_pcov=1,
      comps_fcov=1,
      multiseries_offset=0,
      pcov_offset=0,
      fcov_offset=0,
      comps_stride=100,
      type_stride=10000,
      series_stride=1000000,
      target_start_value=1,
      first_target_start_date=pd.Timestamp("2000-01-01"),
      freq="D",
      integer_index=False,
  ):
      targets, pcovs, fcovs = [], [], []
      for series_idx in range(n_series):
          target_start_date = (
              series_idx * multiseries_offset
              if integer_index
              else first_target_start_date
              + pd.Timedelta(series_idx * multiseries_offset, unit=freq)
          )
          pcov_start_date = (
              target_start_date + pcov_offset
              if integer_index
              else target_start_date
              + pd.Timedelta(pcov_offset, unit=freq)
          )
          fcov_start_date = (
              target_start_date + fcov_offset
              if integer_index
              else target_start_date
              + pd.Timedelta(fcov_offset, unit=freq)
          )
  
          target_start_val = (
              target_start_value + series_stride * series_idx
          )
          pcov_start_val = target_start_val + type_stride
          fcov_start_val = target_start_val + 2 * type_stride
  
          target_ts = None
          pcov_ts = None
          fcov_ts = None
  
          for idx in range(comps_target):
              start = target_start_val + idx * comps_stride
              curr_ts = tg.linear_timeseries(
                  start_value=start,
                  end_value=start + length - 1,
                  start=target_start_date,
                  length=length,
                  freq=freq,
                  column_name=f"{series_idx}-trgt-{idx}",
              )
              target_ts = (
                  target_ts.stack(curr_ts) if target_ts else curr_ts
              )
          for idx in range(comps_pcov):
              start = pcov_start_val + idx * comps_stride
              curr_ts = tg.linear_timeseries(
                  start_value=start,
                  end_value=start + length - 1,
                  start=pcov_start_date,
                  length=length,
                  freq=freq,
                  column_name=f"{series_idx}-pcov-{idx}",
              )
              pcov_ts = pcov_ts.stack(curr_ts) if pcov_ts else curr_ts
          for idx in range(comps_fcov):
              start = fcov_start_val + idx * comps_stride
              curr_ts = tg.linear_timeseries(
                  start_value=start,
                  end_value=start + length - 1,
                  start=fcov_start_date,
                  length=length,
                  freq=freq,
                  column_name=f"{series_idx}-fcov-{idx}",
              )
              fcov_ts = fcov_ts.stack(curr_ts) if fcov_ts else curr_ts
  
          targets.append(target_ts)
          pcovs.append(pcov_ts)
          fcovs.append(fcov_ts)
  
      return targets, pcovs, fcovs
  
imports: import copy
  import functools
  import itertools
  import math
  from unittest.mock import patch
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  import darts
  from darts import TimeSeries
  from darts.dataprocessing.encoders import FutureCyclicEncoder, PastDatetimeAttributeEncoder
  from darts.logging import get_logger
  from darts.metrics import mae, rmse
  from darts.models import CatBoostModel, LightGBMModel, LinearRegressionModel, NotImportedModule, RandomForest, RegressionModel, XGBModel
  from darts.utils import timeseries_generation
  from darts.utils.multioutput import MultiOutputRegressor
label_desc: Use image node_id 2 for calling a global function with example usage: dummy_timeseries(length, n_series, comps_target, comps_pcov, comps_fcov, multiseries_offset, pcov_offset, fcov_offset, comps_stride, type_stride, series_stride, target_start_value, first_target_start_date, freq, integer_index) and returns: targets,  pcovs,  fcovs