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 __iter__(self):
      source_data = self.iterable
      if self.deepcopy:
          try:
              source_data = copy.deepcopy(self.iterable)
          # For the case that data cannot be deep-copied,
          # all in-place operations will affect iterable variable.
          # When this DataPipe is iterated second time, it will
          # yield modified items.
          except TypeError:
              warnings.warn(
                  "The input iterable can not be deepcopied, "
                  "please be aware of in-place modification would affect source data."
              )
      yield from source_data
  
imports: import copy
  import warnings
  from torch.utils.data.datapipes.datapipe import IterDataPipe
label_desc: Use image node_id 2 for calling the IterableWrapperIterDataPipe obj's underlying member method code with example usage: obj.__iter__() without return types