class_docstr: Cover all the utilities involved to export ONNX models
class_docstr_tok: ["Cover","all","the","utilities","involved","to","export","ONNX","models"]
docstr: This test ensures we compute a "correct" approximation of the underlying storage requirement (size) for all the
  parameters for the specified parameter's dtype.
docstr_tok: ["This","test","ensures","we","compute","a","``","correct","''","approximation","of","the","underlying","storage","requirement","(","size",")","for","all","the","parameters","for","the","specified","parameter","'s","dtype","."]
code: def test_compute_parameters_serialized_size(self):
      """
      This test ensures we compute a "correct" approximation of the underlying storage requirement (size) for all the
      parameters for the specified parameter's dtype.
      """
      self.assertEqual(
          compute_serialized_parameters_size(2, ParameterFormat.Float),
          2 * ParameterFormat.Float.size,
      )
  
imports: import os
  from pathlib import Path
  from tempfile import NamedTemporaryFile
  from unittest import TestCase
  from unittest.mock import patch
  import pytest
  from parameterized import parameterized
  from transformers import AutoConfig, PreTrainedTokenizerBase, is_tf_available, is_torch_available
  from transformers.onnx import EXTERNAL_DATA_FORMAT_SIZE_LIMIT, OnnxConfig, OnnxConfigWithPast, ParameterFormat, export, validate_model_outputs
  from transformers.onnx.utils import compute_effective_axis_dimension, compute_serialized_parameters_size, get_preprocessor
  from transformers.testing_utils import require_onnx, require_rjieba, require_tf, require_torch, require_vision, slow
label_desc: Use image node_id 3 for calling the OnnxUtilsTestCaseV2 obj's underlying member method code with example usage: obj.test_compute_parameters_serialized_size() without return types