code: def __init__(self):
      super().__init__()
      self.emb = torch.nn.Embedding(100, 10)
      self.linear1 = torch.nn.Linear(10, 10)
      self.relu = torch.nn.ReLU()
      self.linear2 = torch.nn.Linear(10, 20)
      self.softmax = torch.nn.Softmax(dim=-1)
  
      # transformers.utils.fx.HFTracer needs those.
      self.device = torch.device("cpu")
      self.config = PretrainedConfig(hidden_size=10)
  
file_path: optimum/tests/fx/test_quantization.py
imports: import unittest
  import torch
  from torch.ao.quantization.quantize_fx import fuse_fx
  from torch.ao.quantization.quantize_fx import prepare_fx
  from torch.ao.quantization.quantize_fx import prepare_qat_fx
  from transformers import PretrainedConfig
  from optimum.fx.utils import are_fx_features_available
label_desc: Use image node_id 1 to create a new DummyModel object from inherited base classes: torch.nn with example: obj = DummyModel()