class_docstr: Wraps an iterable object to create an IterDataPipe.
  
  Args:
      iterable: Iterable object to be wrapped into an IterDataPipe
      deepcopy: Option to deepcopy input iterable object for each
          iterator. The copy is made when the first element is read in ``iter()``.
  
  .. note::
      If ``deepcopy`` is explicitly set to ``False``, users should ensure
      that the data pipeline doesn't contain any in-place operations over
      the iterable instance to prevent data inconsistency across iterations.
  
  Example:
      >>> # xdoctest: +SKIP
      >>> from torchdata.datapipes.iter import IterableWrapper
      >>> dp = IterableWrapper(range(10))
      >>> list(dp)
      [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
code: def __len__(self):
      return len(self.iterable)
  
imports: import copy
  import warnings
  from torch.utils.data.datapipes.datapipe import IterDataPipe
label_desc: Use image node_id 3 for calling the IterableWrapperIterDataPipe obj's underlying member method code with example usage: obj.__len__() and returns: len