code: def test_embedding_from_pretrained_padding_idx(self):
      padding_idx = 2
      padding_vec = torch.ones(3) * 7
      embeddings = torch.rand(4, 3, requires_grad=True)
      with torch.no_grad():
          embeddings[padding_idx] = padding_vec
      embedding_nn = nn.Embedding.from_pretrained(
          embeddings, padding_idx=padding_idx
      )
      self.assertEqual(embedding_nn.weight[padding_idx], padding_vec)
  
imports: import unittest
  import random
  import itertools
  from itertools import product
  import torch
  from torch.testing._internal.common_utils import run_tests, set_default_dtype, skipIfTorchDynamo, instantiate_parametrized_tests, parametrize, _assertGradAndGradgradChecks, IS_JETSON
  from torch.testing._internal.common_cuda import TEST_CUDA
  from torch.testing._internal.common_nn import NNTestCase
  from torch.testing._internal.common_device_type import onlyNativeDeviceTypes, dtypes, instantiate_device_type_tests, dtypesIfCUDA, onlyCUDA, TEST_WITH_ROCM, skipCUDAIf, skipMeta
  import torch.nn.functional
  import torch.nn
  from torch.testing._internal.common_utils import dtype2prec_DONTUSE
label_desc: Use image node_id 8 for calling the TestEmbeddingNN obj's underlying member method code with example usage: obj.test_embedding_from_pretrained_padding_idx() without return types