class_docstr: Samples mini-batches randomly but in a grouped manner.
  
  This means that the items from the different groups are always sampled together.
  This is an abstract class. Implement the :py:meth:`~get_groups` method which creates groups to be sampled from.
code: def __len__(self):
      return len(self._group_index)
  
file_path: pytorch-forecasting/pytorch_forecasting/data/samplers.py
imports: import warnings
  import numpy
  import pandas
  from sklearn.utils import shuffle
  import torch
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 5 for calling the GroupedSampler obj's underlying member method code with example usage: obj.__len__() and returns: len