class_docstr: The tests in this section import code from the quantization docs and check that
  they actually run without errors. In cases where objects are undefined in the code snippet,
  they must be provided in the test. The imports seem to behave a bit inconsistently,
  they can be imported either in the test file or passed as a global input
class_docstr_tok: ["The","tests","in","this","section","import","code","from","the","quantization","docs","and","check","that","they","actually","run","without","errors",".","In","cases","where","objects","are","undefined","in","the","code","snippet",",","they","must","be","provided","in","the","test",".","The","imports","seem","to","behave","a","bit","inconsistently",",","they","can","be","imported","either","in","the","test","file","or","passed","as","a","global","input"]
code: def test_quantization_doc_fx(self):
      path_from_pytorch = "docs/source/quantization.rst"
      unique_identifier = "FXPTQ API Example::"
  
      input_fp32 = SingleLayerLinearModel().get_example_inputs()
      global_inputs = {
          "UserModel": SingleLayerLinearModel,
          "input_fp32": input_fp32,
      }
  
      code = self._get_code(path_from_pytorch, unique_identifier)
      self._test_code(code, global_inputs)
  
file_path: pytorch/test/quantization/core/test_docs.py
imports: import re
  import contextlib
  from pathlib import Path
  import torch
  from torch.testing._internal.common_quantization import QuantizationTestCase, SingleLayerLinearModel
  from torch.testing._internal.common_quantized import override_quantized_engine
  from torch.testing._internal.common_utils import IS_ARM64
label_desc: Use image node_id 7 for calling the TestQuantizationDocs obj's underlying member method code with example usage: obj.test_quantization_doc_fx() without return types