class_docstr: Model exporter exports a PyTorch model to Caffe2 model using ONNX
  
  Attributes:
      input_names (List[Str]): names of the input variables to model forward
          function, in a flattened way.
          e.g: forward(tokens, dict) where tokens is List[Tensor] and dict is
          a tuple of value and length: (List[Tensor], List[Tensor]) the
          input names should looks like ['token', 'dict_value', 'dict_length']
      dummy_model_input (Tuple[torch.Tensor]): dummy values to define the
          shape of input tensors, should exactly match the shape of the model
          forward function
      vocab_map (Dict[str, List[str]]): dict of input feature names
          to corresponding index_to_string array, e.g:
          ::
  
              {
                  "text": ["<UNK>", "W1", "W2", "W3", "W4", "W5", "W6", "W7", "W8"],
                  "dict": ["<UNK>", "D1", "D2", "D3", "D4", "D5", "D6", "D7", "D8"]
              }
      output_names (List[Str]): names of output variables
code: def _get_exportable_metadata(
      cls,
      exportable_filter: Callable,
      feature_config: FeatureConfig,
      feature_meta: Dict[str, FieldMeta],
  ) -> Tuple[List[str], List, Dict]:
      # The number of names in input_names *must* be equal to the number of
      # tensors passed in dummy_input
      input_names: List[str] = []
      dummy_model_input: List = []
      feature_itos_map = {}
  
      for name, feat_config in feature_config._asdict().items():
          if exportable_filter(feat_config):
              input_names.extend(feat_config.export_input_names)
              if getattr(feature_meta[name], "vocab", None):
                  feature_itos_map[
                      feat_config.export_input_names[0]
                  ] = feature_meta[name].vocab.itos
              dummy_model_input.append(
                  feature_meta[name].dummy_model_input
              )
      return input_names, dummy_model_input, feature_itos_map
  
imports: import tempfile
  from typing import Callable, Dict, List, Tuple, Union
  import torch
  from caffe2.python import core
  from caffe2.python.onnx.backend_rep import Caffe2Rep
  from pytext.config import ConfigBase
  from pytext.config.component import Component, ComponentType
  from pytext.config.field_config import FeatureConfig
  from pytext.data import CommonMetadata
  from pytext.fields import FieldMeta
  from pytext.utils import onnx
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 9 for calling the ModelExporter obj's underlying member method code with example usage: obj._get_exportable_metadata(cls, exportable_filter, feature_config, feature_meta) and returns: input_names,  dummy_model_input,  feature_itos_map