class_docstr: Given an input shaped like ``(batch_size, time_steps, [rest])`` and a ``Module`` that takes
  inputs like ``(batch_size, [rest])``, ``TimeDistributed`` reshapes the input to be
  ``(batch_size * time_steps, [rest])``, applies the contained ``Module``, then reshapes it back.
  
  Note that while the above gives shapes with ``batch_size`` first, this ``Module`` also works if
  ``batch_size`` is second - we always just combine the first two dimensions, then split them.
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code: def __init__(self, module):
      super(TimeDistributed, self).__init__()
      self._module = module
  
imports: from __future__ import absolute_import
  import torch
label_desc: Use image node_id 1 to create a new TimeDistributed object from inherited base classes: torch.nn with example: obj = TimeDistributed(module)