class_docstr: Wraps a sequence object into a MapDataPipe.
  
  Args:
      sequence: Sequence object to be wrapped into an MapDataPipe
      deepcopy: Option to deepcopy input sequence object
  
  .. note::
    If ``deepcopy`` is set to False explicitly, users should ensure
    that data pipeline doesn't contain any in-place operations over
    the iterable instance, in order to prevent data inconsistency
    across iterations.
  
  Example:
      >>> # xdoctest: +SKIP
      >>> from torchdata.datapipes.map import SequenceWrapper
      >>> dp = SequenceWrapper(range(10))
      >>> list(dp)
      [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
      >>> dp = SequenceWrapper({'a': 100, 'b': 200, 'c': 300, 'd': 400})
      >>> dp['a']
      100
code: def __len__(self):
      return len(self.sequence)
  
imports: import copy
  import warnings
  from torch.utils.data.datapipes.datapipe import MapDataPipe
label_desc: Use image node_id 3 for calling the SequenceWrapperMapDataPipe obj's underlying member method code with example usage: obj.__len__() and returns: len