class_docstr: Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
docstr: Flag indicating if the model requires using external data format
  
  Args:
      num_parameters: Number of parameter on the model
  
  Returns:
      True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise
docstr_tok: ["Flag","indicating","if","the","model","requires","using","external","data","format","Args",":","num_parameters",":","Number","of","parameter","on","the","model","Returns",":","True","if","model.num_parameters","(",")","*","size_of","(","float32",")",">","=","2Gb","False","otherwise"]
code: def use_external_data_format(num_parameters: int) -> bool:
      """
      Flag indicating if the model requires using external data format
  
      Args:
          num_parameters: Number of parameter on the model
  
      Returns:
          True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise
      """
  
      return (
          compute_serialized_parameters_size(
              num_parameters, ParameterFormat.Float
          )
          >= EXTERNAL_DATA_FORMAT_SIZE_LIMIT
      )
  
imports: import copy
  import dataclasses
  import warnings
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union
  import numpy
  from packaging import version
  from ..utils import TensorType, is_torch_available, is_vision_available, logging
  from .utils import ParameterFormat, compute_effective_axis_dimension, compute_serialized_parameters_size
label_desc: Use image node_id 12 for calling the OnnxConfig obj's underlying member method code with example usage: obj.use_external_data_format(num_parameters) and returns: unknown