class_docstr: TimeSeriesLoader DataLoader.
  [Source code](https://github.com/Nixtla/neuralforecast1/blob/main/neuralforecast/tsdataset.py).
  
  Small change to PyTorch's Data loader.
  Combines a dataset and a sampler, and provides an iterable over the given dataset.
  
  The class `~torch.utils.data.DataLoader` supports both map-style and
  iterable-style datasets with single- or multi-process loading, customizing
  loading order and optional automatic batching (collation) and memory pinning.
  
  **Parameters:**<br>
  `batch_size`: (int, optional): how many samples per batch to load (default: 1).<br>
  `shuffle`: (bool, optional): set to `True` to have the data reshuffled at every epoch (default: `False`).<br>
  `sampler`: (Sampler or Iterable, optional): defines the strategy to draw samples from the dataset.<br>
              Can be any `Iterable` with `__len__` implemented. If specified, `shuffle` must not be specified.<br>
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code: def __init__(self, dataset, **kwargs):
      if "collate_fn" in kwargs:
          kwargs.pop("collate_fn")
      kwargs_ = {**kwargs, **dict(collate_fn=self._collate_fn)}
      DataLoader.__init__(self, dataset=dataset, **kwargs_)
  
imports: import warnings
  from collections.abc import Mapping
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  import utilsforecast.processing
  from torch.utils.data import Dataset, DataLoader
  from utilsforecast.compat import DataFrame, pl_Series
label_desc: Use image node_id 1 to create a new TimeSeriesLoader object from inherited base classes: DataLoader with example: obj = TimeSeriesLoader(dataset)