class_docstr: Abstract implementation of encrypted tensor type. Every subclass of `CrypTensor`
  must implement the methods defined here. The actual tensor data should live in
  an instance attribute called `_tensor`. When implemented, the `CrypTensor`
  provides a full autograd implementation to the user.
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docstr: Subtracts a :attr:`tensor` from :attr:`self` tensor.
  The shape of :attr:`tensor` must be
  `broadcastable`_ with the shape of :attr:`self`.
  
  .. _broadcastable:
      https://pytorch.org/docs/stable/notes/broadcasting.html#broadcasting-semantics
docstr_tok: ["Subtracts","a",":","attr",":","`","tensor","`","from",":","attr",":","`","self","`","tensor",".","The","shape","of",":","attr",":","`","tensor","`","must","be","`","broadcastable","`","_","with","the","shape","of",":","attr",":","`","self","`",".","..","_broadcastable",":","https",":","\/\/pytorch.org\/docs\/stable\/notes\/broadcasting.html","#","broadcasting-semantics"]
code: def sub(self, tensor):
      """Subtracts a :attr:`tensor` from :attr:`self` tensor.
      The shape of :attr:`tensor` must be
      `broadcastable`_ with the shape of :attr:`self`.
  
      .. _broadcastable:
          https://pytorch.org/docs/stable/notes/broadcasting.html#broadcasting-semantics
      """
      return self.add(-tensor)
  
imports: from contextlib import contextmanager
  import torch
  from .debug import register_validation
  from .gradients import AutogradContext, BaseAutogradContext, get_grad_fn
  from .None import functions
label_desc: Use image node_id 20 for calling the CrypTensor obj's underlying member method code with example usage: obj.sub(tensor) and returns: self