code: def make_dataloaders(data_with_covariates, **kwargs):
      training_cutoff = "2016-09-01"
      max_encoder_length = 4
      max_prediction_length = 3
  
      kwargs.setdefault("target", "volume")
      kwargs.setdefault("group_ids", ["agency", "sku"])
      kwargs.setdefault("add_relative_time_idx", True)
      kwargs.setdefault("time_varying_unknown_reals", ["volume"])
  
      training = TimeSeriesDataSet(
          data_with_covariates[
              lambda x: x.date < training_cutoff
          ].copy(),
          time_idx="time_idx",
          max_encoder_length=max_encoder_length,
          max_prediction_length=max_prediction_length,
          **kwargs,  # fixture parametrization
      )
  
      validation = TimeSeriesDataSet.from_dataset(
          training,
          data_with_covariates.copy(),
          min_prediction_idx=training.index.time.max() + 1,
      )
      train_dataloader = training.to_dataloader(
          train=True, batch_size=2, num_workers=0
      )
      val_dataloader = validation.to_dataloader(
          train=False, batch_size=2, num_workers=0
      )
      test_dataloader = validation.to_dataloader(
          train=False, batch_size=1, num_workers=0
      )
  
      return dict(
          train=train_dataloader,
          val=val_dataloader,
          test=test_dataloader,
      )
  
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 3 for calling a global function with example usage: make_dataloaders(data_with_covariates) and returns: dict