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: Resets gradient information in tensor.
docstr_tok: ["Resets","gradient","information","in","tensor","."]
code: def _reset_gradients(self):
      """Resets gradient information in tensor."""
      self.grad = None  # gradient itself
      self.grad_fn = None  # functions to call for gradient
      self.grad_expected = (
          0  # number of gradients expected from parents
      )
      self.grad_received = (
          0  # number of gradients received from parents
      )
      self.children = []  # children of node in graph
      self.ctx = AutogradContext()
  
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 7 for calling the CrypTensor obj's underlying member method code with example usage: obj._reset_gradients() without return types