code: def test_embedding_bag_from_pretrained_padding_idx(self):
      padding_idx = 2
      embeddings = torch.rand(4, 3, requires_grad=True)
      embedding_nn = nn.EmbeddingBag.from_pretrained(
          embeddings, padding_idx=padding_idx
      )
      self.assertEqual(embedding_nn.weight, embeddings)
  
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 9 for calling the TestEmbeddingNN obj's underlying member method code with example usage: obj.test_embedding_bag_from_pretrained_padding_idx() without return types