code: def dataloaders_with_different_encoder_decoder_length(
      data_with_covariates,
  ):
      return make_dataloaders(
          data_with_covariates.copy(),
          target="target",
          time_varying_known_categoricals=["special_days", "month"],
          variable_groups=dict(
              special_days=[
                  "easter_day",
                  "good_friday",
                  "new_year",
                  "christmas",
                  "labor_day",
                  "independence_day",
                  "revolution_day_memorial",
                  "regional_games",
                  "fifa_u_17_world_cup",
                  "football_gold_cup",
                  "beer_capital",
                  "music_fest",
              ]
          ),
          time_varying_known_reals=[
              "time_idx",
              "price_regular",
              "price_actual",
              "discount",
              "discount_in_percent",
          ],
          time_varying_unknown_categoricals=[],
          time_varying_unknown_reals=[
              "target",
              "volume",
              "log_volume",
              "industry_volume",
              "soda_volume",
              "avg_max_temp",
          ],
          static_categoricals=["agency"],
          add_relative_time_idx=False,
          target_normalizer=GroupNormalizer(
              groups=["agency", "sku"], center=False
          ),
      )
  
file_path: pytorch-forecasting/tests/test_models/conftest.py
imports: import numpy
  import pytest
  import torch
  from pytorch_forecasting import TimeSeriesDataSet
  from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, NaNLabelEncoder
  from pytorch_forecasting.data.examples import generate_ar_data, get_stallion_data
label_desc: Use image node_id 5 for calling a global function with example usage: dataloaders_with_different_encoder_decoder_length(data_with_covariates) and returns: make_dataloaders