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: Returns a copy of the :attr:`self` tensor.
  The copy has the same size and data type as :attr:`self`.
  
  .. note::
      This function is recorded in the computation graph. Gradients
      propagating to the cloned tensor will propagate to the original tensor.
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code: def clone(self):
      """
      Returns a copy of the :attr:`self` tensor.
      The copy has the same size and data type as :attr:`self`.
  
      .. note::
          This function is recorded in the computation graph. Gradients
          propagating to the cloned tensor will propagate to the original tensor.
      """
      raise NotImplementedError("clone is not implemented")
  
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 44 for calling the CrypTensor obj's underlying member method code with example usage: obj.clone() without return types