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.
class_docstr_tok: ["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","."]
docstr: Enables (`mode = True`) or disables (`mode = False`) Crypten's autograd.
docstr_tok: ["Enables","(","`","mode","=","True","`",")","or","disables","(","`","mode","=","False","`",")","Crypten","'s","autograd","."]
code: def set_grad_enabled(mode):
      """
      Enables (`mode = True`) or disables (`mode = False`) Crypten's autograd.
      """
      CrypTensor.AUTOGRAD_ENABLED = mode
  
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 4 for calling the CrypTensor obj's underlying member method code with example usage: obj.set_grad_enabled(mode) without return types