code: def test_embedding_ops_dynamic(self):
      class EmbeddingWithLinear(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.emb = torch.nn.Embedding(
                  num_embeddings=10,
                  embedding_dim=12,
                  scale_grad_by_freq=False,
              )
              self.fc = torch.nn.Linear(5, 5)
  
          def forward(self, indices, linear_in):
              return self.emb(indices), self.fc(linear_in)
  
      model = EmbeddingWithLinear().eval()
      qconfig_dict = {
          torch.nn.Embedding: float_qparams_weight_only_qconfig,
          torch.nn.Linear: default_dynamic_qconfig,
      }
      indices = torch.tensor(
          [
              9,
              6,
              5,
              7,
              8,
              8,
              9,
              2,
              8,
              6,
              6,
              9,
              1,
              6,
              8,
              8,
              3,
              2,
              3,
              6,
              3,
              6,
              5,
              7,
              0,
              8,
              4,
              6,
              5,
              8,
              2,
              3,
          ]
      )
      q_model = quantize_dynamic(model, qconfig_dict)
      self.assertTrue("QuantizedEmbedding" in str(q_model.emb))
      self.assertTrue("DynamicQuantizedLinear" in str(q_model.fc))
      q_model(indices, torch.randn(5, 5))
  
imports: import torch
  import torch.nn
  import torch.ao.nn.quantized
  from torch.nn.utils.rnn import PackedSequence
  from torch.ao.quantization import quantize, prepare, convert, prepare_qat, quantize_dynamic, QuantWrapper, QuantStub, DeQuantStub, default_qconfig, default_dynamic_qconfig, per_channel_dynamic_qconfig, float16_dynamic_qconfig, float_qparams_weight_only_qconfig, float_qparams_weight_only_qconfig_4bit, FixedQParamsObserver, PerChannelMinMaxObserver, default_dynamic_quant_observer, default_weight_observer, QConfig
  from torch.testing._internal.common_quantization import QuantizationTestCase, AnnotatedSingleLayerLinearModel, QuantStubModel, ModelWithFunctionals, SingleLayerLinearDynamicModel, TwoLayerLinearModel, NestedModel, ResNetBase, RNNDynamicModel, RNNCellDynamicModel, ActivationsTestModel, NormalizationTestModel, test_only_eval_fn, prepare_dynamic, convert_dynamic, skipIfNoFBGEMM, EmbeddingBagModule, EmbeddingModule, EmbeddingWithStaticLinear, LinearReluLinearModel
  from torch.testing._internal.common_quantization import AnnotatedTwoLayerLinearModel, AnnotatedNestedModel, AnnotatedSubNestedModel, AnnotatedCustomConfigNestedModel, AnnotatedSkipQuantModel
  from torch.testing._internal.common_quantized import override_quantized_engine, supported_qengines, override_qengines
  from hypothesis import given
  from hypothesis import strategies
  import torch.testing._internal.hypothesis_utils
  from typing import Tuple
  import numpy
label_desc: Use image node_id 13 for calling the TestQuantizeEagerPTQDynamic obj's underlying member method code with example usage: obj.test_embedding_ops_dynamic() and returns: self,  self