class_docstr: Base container for ONNX models.
  The container allows to mirror the Sklearn API.
class_docstr_tok: ["Base","container","for","ONNX","models",".","The","container","allows","to","mirror","the","Sklearn","API","."]
docstr: Retrieve the inputs names from the session object.
docstr_tok: ["Retrieve","the","inputs","names","from","the","session","object","."]
code: def _get_named_inputs(self, inputs):
      """
      Retrieve the inputs names from the session object.
      """
      if len(inputs) < len(self._input_names):
          inputs = inputs[0]
  
      assert len(inputs) == len(self._input_names)
  
      named_inputs = {}
  
      for i in range(len(inputs)):
          input_ = np.array(inputs[i])
          if input_.dtype.kind in constants.SUPPORTED_STRING_TYPES:
              assert constants.MAX_STRING_LENGTH in self._extra_config
  
              input_ = from_strings_to_ints(
                  input_,
                  self._extra_config[constants.MAX_STRING_LENGTH],
              )
          named_inputs[self._input_names[i]] = input_
  
      return named_inputs
  
imports: import pickle
  import os
  import numpy
  import shutil
  import torch
  import warnings
  import hummingbird
  from hummingbird.ml._utils import onnx_runtime_installed, from_strings_to_ints, dump_versions, check_dumped_versions
  from hummingbird.ml.operator_converters import constants
  from hummingbird.ml.containers._sklearn_api_containers import SklearnContainer, SklearnContainerTransformer, SklearnContainerRegression, SklearnContainerClassification, SklearnContainerAnomalyDetection
  import onnx
label_desc: Use image node_id 4 for calling the ONNXSklearnContainer obj's underlying member method code with example usage: obj._get_named_inputs(inputs) and returns: named_inputs