code: def __init__(self, path, providers=None):
      import onnxruntime
  
      # Get the model meta data from the MLModel yaml file which may contain the providers
      # specification.
      local_path = str(Path(path).parent)
      model_meta = Model.load(
          os.path.join(local_path, MLMODEL_FILE_NAME)
      )
  
      # Check if the MLModel config has the providers meta data
      if "providers" in model_meta.flavors.get(FLAVOR_NAME).keys():
          providers = model_meta.flavors.get(FLAVOR_NAME)["providers"]
      # If not, then default to the predefined list.
      else:
          providers = ONNX_EXECUTION_PROVIDERS
  
      sess_options = onnxruntime.SessionOptions()
      options = model_meta.flavors.get(FLAVOR_NAME).get(
          "onnx_session_options"
      )
      if options:
          if inter_op_num_threads := options.get(
              "inter_op_num_threads"
          ):
              sess_options.inter_op_num_threads = inter_op_num_threads
          if intra_op_num_threads := options.get(
              "intra_op_num_threads"
          ):
              sess_options.intra_op_num_threads = intra_op_num_threads
          if execution_mode := options.get("execution_mode"):
              if execution_mode.upper() == "SEQUENTIAL":
                  sess_options.execution_mode = (
                      onnxruntime.ExecutionMode.ORT_SEQUENTIAL
                  )
              elif execution_mode.upper() == "PARALLEL":
                  sess_options.execution_mode = (
                      onnxruntime.ExecutionMode.ORT_PARALLEL
                  )
          if graph_optimization_level := options.get(
              "graph_optimization_level"
          ):
              sess_options.graph_optimization_level = (
                  onnxruntime.GraphOptimizationLevel(
                      graph_optimization_level
                  )
              )
          if extra_session_config := options.get(
              "extra_session_config"
          ):
              for key, value in extra_session_config.items():
                  sess_options.add_session_config_entry(key, value)
  
      # NOTE: Some distributions of onnxruntime require the specification of the providers
      # argument on calling. E.g. onnxruntime-gpu. The package import call does not differentiate
      #  which architecture specific version has been installed, as all are imported with
      # onnxruntime. onnxruntime documentation says that from v1.9.0 some distributions require
      #  the providers list to be provided on calling an InferenceSession. Therefore the try
      #  catch structure below attempts to create an inference session with just the model path
      #  as pre v1.9.0. If that fails, it will use the providers list call.
      # At the moment this is just CUDA and CPU, and probably should be expanded.
      # A method of user customization has been provided by adding a variable in the save_model()
      # function, which allows the ability to pass the list of execution providers via a
      # optional argument e.g.
      #
      # mlflow.onnx.save_model(..., providers=['CUDAExecutionProvider'...])
      #
      # For details of the execution providers construct of onnxruntime, see:
      # https://onnxruntime.ai/docs/execution-providers/
      #
      # For a information on how execution providers are used with onnxruntime InferenceSession,
      # see the API page below:
      # https://onnxruntime.ai/docs/api/python/api_summary.html#id8
      #
  
      try:
          self.rt = onnxruntime.InferenceSession(
              path, sess_options=sess_options
          )
      except ValueError:
          self.rt = onnxruntime.InferenceSession(
              path, providers=providers, sess_options=sess_options
          )
  
      assert len(self.rt.get_inputs()) >= 1
      self.inputs = [
          (inp.name, inp.type) for inp in self.rt.get_inputs()
      ]
      self.output_names = [outp.name for outp in self.rt.get_outputs()]
  
imports: import logging
  import os
  from pathlib import Path
  from typing import Any, Dict, Optional
  import numpy
  import pandas
  import yaml
  from packaging.version import Version
  import mlflow.tracking
  from mlflow import pyfunc
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelInputExample, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import _save_example
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import get_total_file_size, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path, _validate_onnx_session_options
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 1 to create a new _OnnxModelWrapper object  with example: obj = _OnnxModelWrapper(path, providers)