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: Creates a new `CrypTensor` object. The `requires_grad` flag determines
  if computations on the created tensor are logged on the autograd tape.
  
  NOTE: This constructor cannot be called directly. It is only be called
  via `super()` from classes that implement the `CrypTensor` abstraction.
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code: def __init__(self, requires_grad=False):
      """
      Creates a new `CrypTensor` object. The `requires_grad` flag determines
      if computations on the created tensor are logged on the autograd tape.
  
      NOTE: This constructor cannot be called directly. It is only be called
      via `super()` from classes that implement the `CrypTensor` abstraction.
      """
      self.requires_grad = (
          requires_grad  # whether tensors needs gradient
      )
      self._reset_gradients()
  
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 5 to create a new CrypTensor object from inherited base classes: object with example: obj = CrypTensor(requires_grad)