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: Element-wise divide by a tensor.
docstr_tok: ["Element-wise","divide","by","a","tensor","."]
code: def __itruediv__(self, scalar):
      """Element-wise divide by a tensor."""
      return self.div_(scalar)
  
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 28 for calling the CrypTensor obj's underlying member method code with example usage: obj.__itruediv__(scalar) and returns: self