code: def test_dirac_identity(self):
      for groups in [1, 3]:
          batch, in_c, out_c, size, kernel_size = (
              8,
              3,
              9,
              5,
              3,
          )  # in_c, out_c must divide by groups
          eff_out_c = out_c // groups
  
          # Test 1D
          input_var = torch.randn(batch, in_c, size)
          filter_var = torch.zeros(eff_out_c, in_c, kernel_size)
          filter_var = torch.cat([filter_var] * groups)
          init.dirac_(filter_var, groups)
          output_var = F.conv1d(input_var, filter_var)
          input_tensor, output_tensor = (
              input_var.data,
              output_var.data,
          )  # Variables do not support nonzero
          for g in range(groups):
              # Assert in_c outputs are preserved (per each group)
              self.assertEqual(
                  input_tensor[:, :, 1:-1],
                  output_tensor[
                      :, eff_out_c * g : eff_out_c * g + in_c, :
                  ],
              )
              # Assert extra outputs are 0
              assert (
                  torch.nonzero(
                      output_tensor[
                          :,
                          eff_out_c * g + in_c : eff_out_c * (g + 1),
                          :,
                      ]
                  ).numel()
                  == 0
              )
  
          # Test 2D
          input_var = torch.randn(batch, in_c, size, size)
          filter_var = torch.zeros(
              eff_out_c, in_c, kernel_size, kernel_size
          )
          filter_var = torch.cat([filter_var] * groups)
          init.dirac_(filter_var, groups)
          output_var = F.conv2d(input_var, filter_var)
          input_tensor, output_tensor = (
              input_var.data,
              output_var.data,
          )  # Variables do not support nonzero
          for g in range(groups):
              # Assert in_c outputs are preserved (per each group)
              self.assertEqual(
                  input_tensor[:, :, 1:-1, 1:-1],
                  output_tensor[
                      :, eff_out_c * g : eff_out_c * g + in_c, :, :
                  ],
              )
              # Assert extra outputs are 0
              assert (
                  torch.nonzero(
                      output_tensor[
                          :,
                          eff_out_c * g + in_c : eff_out_c * (g + 1),
                          :,
                          :,
                      ]
                  ).numel()
                  == 0
              )
  
          # Test 3D
          input_var = torch.randn(batch, in_c, size, size, size)
          filter_var = torch.zeros(
              eff_out_c, in_c, kernel_size, kernel_size, kernel_size
          )
          filter_var = torch.cat([filter_var] * groups)
          init.dirac_(filter_var, groups)
          output_var = F.conv3d(input_var, filter_var)
          input_tensor, output_tensor = input_var.data, output_var.data
          for g in range(groups):
              # Assert in_c outputs are preserved (per each group)
              self.assertEqual(
                  input_tensor[:, :, 1:-1, 1:-1, 1:-1],
                  output_tensor[
                      :, eff_out_c * g : eff_out_c * g + in_c, :, :, :
                  ],
              )
              # Assert extra outputs are 0
              assert (
                  torch.nonzero(
                      output_tensor[
                          :,
                          eff_out_c * g + in_c : eff_out_c * (g + 1),
                          :,
                          :,
                          :,
                      ]
                  ).numel()
                  == 0
              )
  
imports: import random
  import unittest
  import math
  import string
  from functools import reduce
  from operator import mul
  from torch.testing._internal.common_utils import TestCase, TEST_SCIPY, skipIfNoLapack, skipIfTorchDynamo, run_tests
  import torch
  import torch.nn.init
  import torch.nn.functional
label_desc: Use image node_id 21 for calling the TestNNInit obj's underlying member method code with example usage: obj.test_dirac_identity() without return types