class_docstr: Exporter for models that have DenseFeatures as input to the decoder
class_docstr_tok: ["Exporter","for","models","that","have","DenseFeatures","as","input","to","the","decoder"]
code: def get_feature_metadata(
      cls,
      feature_config: FeatureConfig,
      feature_meta: Dict[str, FieldMeta],
  ):
      # add all features EXCEPT dense features. The features exported here
      # go through the representation layer
      (
          input_names_rep,
          dummy_model_input_rep,
          feature_itos_map_rep,
      ) = cls._get_exportable_metadata(
          lambda x: isinstance(x, ConfigBase)
          and not isinstance(x, FloatVectorConfig),
          feature_config,
          feature_meta,
      )
  
      # need feature lengths only for non-dense features
      cls._add_feature_lengths(input_names_rep, dummy_model_input_rep)
  
      # add dense features. These features don't go through the representation
      # layer, instead they go directly to the decoder
      (
          input_names_dense,
          dummy_model_input_dense,
          feature_itos_map_dense,
      ) = cls._get_exportable_metadata(
          lambda x: isinstance(x, FloatVectorConfig),
          feature_config,
          feature_meta,
      )
  
      feature_itos_map_rep.update(feature_itos_map_dense)
      return (
          input_names_rep + input_names_dense,
          tuple(dummy_model_input_rep + dummy_model_input_dense),
          feature_itos_map_rep,
      )
  
imports: import os
  from typing import Dict, List
  from caffe2.python import core, workspace
  from pytext.config import ConfigBase
  from pytext.config.field_config import FeatureConfig, FloatVectorConfig
  from pytext.config.module_config import ExporterType
  from pytext.exporters.exporter import ModelExporter
  from pytext.fields import FieldMeta
  from pytext.utils import mobile_onnx
  from pytext.utils.file_io import PathManager
label_desc: Use image node_id 1 for calling the DenseFeatureExporter obj's underlying member method code with example usage: obj.get_feature_metadata(cls, feature_config, feature_meta) and returns: unknown,  tuple,  feature_itos_map_rep