class_docstr: OpenVINO implementation for the inference.
  
  Args:
      path (str | Path): Path to the openvino onnx, xml or bin file.
      metadata (str | Path | dict, optional): Path to metadata file or a dict object defining the
          metadata. Defaults to None.
      device (str | None, optional): Device to run the inference on. Defaults to "CPU".
      task (TaskType | None, optional): Task type. Defaults to None.
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docstr: Load the OpenVINO model.
  
  Args:
      path (str | Path | tuple[bytes, bytes]): Path to the onnx or xml and bin files
                                                  or tuple of .xml and .bin data as bytes.
  
  Returns:
      [tuple[str, str, ExecutableNetwork]]: Input and Output blob names
          together with the Executable network.
docstr_tok: ["Load","the","OpenVINO","model",".","Args",":","path","(","str","|","Path","|","tuple","[","bytes",",","bytes","]",")",":","Path","to","the","onnx","or","xml","and","bin","files","or","tuple","of",".xml","and",".bin","data","as","bytes",".","Returns",":","[","tuple","[","str",",","str",",","ExecutableNetwork","]","]",":","Input","and","Output","blob","names","together","with","the","Executable","network","."]
code: def load_model(self, path: str | Path | tuple[bytes, bytes]):
      """Load the OpenVINO model.
  
      Args:
          path (str | Path | tuple[bytes, bytes]): Path to the onnx or xml and bin files
                                                      or tuple of .xml and .bin data as bytes.
  
      Returns:
          [tuple[str, str, ExecutableNetwork]]: Input and Output blob names
              together with the Executable network.
      """
      ie_core = Core()
      # If tuple of bytes is passed
  
      if isinstance(path, tuple):
          model = ie_core.read_model(
              model=path[0], weights=path[1], init_from_buffer=True
          )
      else:
          path = path if isinstance(path, Path) else Path(path)
          if path.suffix in (".bin", ".xml"):
              if path.suffix == ".bin":
                  bin_path, xml_path = path, path.with_suffix(".xml")
              elif path.suffix == ".xml":
                  xml_path, bin_path = path, path.with_suffix(".bin")
              model = ie_core.read_model(xml_path, bin_path)
          elif path.suffix == ".onnx":
              model = ie_core.read_model(path)
          else:
              raise ValueError(
                  f"Path must be .onnx, .bin or .xml file. Got {path.suffix}"
              )
      # Create cache folder
      cache_folder = Path("cache")
      cache_folder.mkdir(exist_ok=True)
      ie_core.set_property({"CACHE_DIR": cache_folder})
  
      compile_model = ie_core.compile_model(
          model=model, device_name=self.device, config=self.config
      )
  
      input_blob = compile_model.input(0)
      output_blob = compile_model.output(0)
  
      return input_blob, output_blob, compile_model
  
file_path: anomalib/src/anomalib/deploy/inferencers/openvino_inferencer.py
imports: from __future__ import annotations
  import logging
  from importlib.util import find_spec
  from pathlib import Path
  from typing import Any
  import albumentations
  import cv2
  import numpy
  from omegaconf import DictConfig
  from anomalib.data import TaskType
  from .base_inferencer import Inferencer
label_desc: Use image node_id 2 for calling the OpenVINOInferencer obj's underlying member method code with example usage: obj.load_model(path) and returns: input_blob,  output_blob,  compile_model