class_docstr: Gets data type information of the ONNX Runtime inference session and provides the mapping from
  `OrtValue` data types to the data types of other frameworks (NumPy, PyTorch, etc).
class_docstr_tok: ["Gets","data","type","information","of","the","ONNX","Runtime","inference","session","and","provides","the","mapping","from","`","OrtValue","`","data","types","to","the","data","types","of","other","frameworks","(","NumPy",",","PyTorch",",","etc",")","."]
code: def ort_type_to_numpy_type(ort_type: str):
      ort_type_to_numpy_type_map = {
          "tensor(int64)": np.int64,
          "tensor(int32)": np.int32,
          "tensor(int8)": np.int8,
          "tensor(float)": np.float32,
          "tensor(float16)": np.float16,
          "tensor(bool)": bool,
      }
      if ort_type in ort_type_to_numpy_type_map:
          return ort_type_to_numpy_type_map[ort_type]
      else:
          raise ValueError(
              f"{ort_type} is not supported. Here is a list of supported data type: {ort_type_to_numpy_type_map.keys()}"
          )
  
imports: import logging
  import traceback
  from typing import TYPE_CHECKING
  import numpy
  import torch
  import onnxruntime
  from onnxruntime.capi.onnxruntime_inference_collection import OrtValue
  from onnxruntime.transformers.io_binding_helper import TypeHelper
  from ..utils import is_cupy_available, is_onnxruntime_training_available
label_desc: Use image node_id 1 for calling the TypeHelper obj's underlying member method code with example usage: obj.ort_type_to_numpy_type(ort_type) and returns: ort_type_to_numpy_type_map