code: def dataloaders_fixed_window_without_covariates():
      data = generate_ar_data(
          seasonality=10.0, timesteps=50, n_series=2
      )
      validation = data.series.iloc[:2]
  
      max_encoder_length = 30
      max_prediction_length = 10
  
      training = TimeSeriesDataSet(
          data[lambda x: ~x.series.isin(validation)],
          time_idx="time_idx",
          target="value",
          categorical_encoders={
              "series": NaNLabelEncoder().fit(data.series)
          },
          group_ids=["series"],
          static_categoricals=[],
          max_encoder_length=max_encoder_length,
          max_prediction_length=max_prediction_length,
          time_varying_unknown_reals=["value"],
          target_normalizer=EncoderNormalizer(),
      )
  
      validation = TimeSeriesDataSet.from_dataset(
          training,
          data[lambda x: x.series.isin(validation)],
          stop_randomization=True,
      )
      batch_size = 2
      train_dataloader = training.to_dataloader(
          train=True, batch_size=batch_size, num_workers=0
      )
      val_dataloader = validation.to_dataloader(
          train=False, batch_size=batch_size, num_workers=0
      )
      test_dataloader = validation.to_dataloader(
          train=False, batch_size=batch_size, 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 8 for calling a global function with example usage: dataloaders_fixed_window_without_covariates() and returns: dict