class_docstr: This class tests all autograd-related functionality.
docstr: Tests the part of autograd take that does not have a torch equivalent
docstr_tok: ["Tests","the","part","of","autograd","take","that","does","not","have","a","torch","equivalent"]
code: def test_autograd_func_take(self):
      """Tests the part of autograd take that does not have a torch equivalent"""
      tensor_size = [5, 5, 5, 5]
      index = torch.tensor(
          [[[1, 2], [3, 4]], [[4, 2], [1, 3]]], dtype=torch.long
      )
  
      # Test when dimension!=None
      for dimension in range(0, 4):
          tensor = get_random_test_tensor(
              size=tensor_size, is_float=True
          )
          ref_forward = torch.from_numpy(
              tensor.numpy().take(index, dimension)
          )
          encrypted_tensor = crypten.cryptensor(tensor)
          encr_inputs = [encrypted_tensor, index, dimension]
  
          # test forward
          ctx = AutogradContext()
          grad_fn_take = gradients.get_grad_fn("take")
          encr_output = grad_fn_take.forward(ctx, *encr_inputs)
          self._check(
              encr_output,
              ref_forward,
              "take forward failed: dimension set",
          )
  
          # test backward:
          # first, recreate take forward function with only torch operations
          tensor2 = get_random_test_tensor(
              size=tensor_size, is_float=True
          )
          tensor2.requires_grad = True
          all_indices = [slice(0, x) for x in tensor2.size()]
          all_indices[dimension] = index
          ref_forward_torch = tensor2[all_indices]
          grad_output = torch.ones(ref_forward_torch.size())
          ref_forward_torch.backward(grad_output)
  
          # next, do backward pass on encrypted tensor
          encr_grad_output = encr_output.new(grad_output)
          encr_grad = grad_fn_take.backward(ctx, encr_grad_output)
  
          # finally, compare values
          self._check(
              encr_grad,
              tensor2.grad,
              "take backward failed: dimension set",
          )
  
imports: import logging
  import random
  import unittest
  import crypten
  import crypten.gradients
  import torch
  from crypten.common.tensor_types import is_float_tensor
  from crypten.config import cfg
  from crypten.gradients import AutogradContext, AutogradFunction
  from test.multiprocess_test_case import get_random_test_tensor, MultiProcessTestCase
label_desc: Use image node_id 6 for calling the TestAutograd obj's underlying member method code with example usage: obj.test_autograd_func_take() without return types