class_docstr: Enables Torch ORT: Accelerate PyTorch models with ONNX Runtime.
  
  Wraps a model with the ORT wrapper, lazily converting your module into an ONNX export, to optimize for
  training and inference.
  
  Usage:
  
      # via Transformer Tasks
      model = TextClassifier(backbone="facebook/bart-large", num_classes=datamodule.num_classes, enable_ort=True)
  
      # or via the trainer
      trainer = flash.Trainer(callbacks=ORTCallback())
class_docstr_tok: ["Enables","Torch","ORT",":","Accelerate","PyTorch","models","with","ONNX","Runtime",".","Wraps","a","model","with","the","ORT","wrapper",",","lazily","converting","your","module","into","an","ONNX","export",",","to","optimize","for","training","and","inference",".","Usage",":","#","via","Transformer","Tasks","model","=","TextClassifier","(","backbone=","''","facebook\/bart-large","''",",","num_classes=datamodule.num_classes",",","enable_ort=True",")","#","or","via","the","trainer","trainer","=","flash.Trainer","(","callbacks=ORTCallback","(",")",")"]
code: def __init__(self):
      if not _TORCH_ORT_AVAILABLE:
          raise ModuleNotFoundError(
              "Torch ORT is required to use ORT. See here for installation: https://github.com/pytorch/ort"
          )
  
imports: from pytorch_lightning import Callback, LightningModule, Trainer
  from flash.core.utilities.imports import _TORCH_ORT_AVAILABLE
label_desc: Use image node_id 1 to create a new ORTCallback object from inherited base classes: Callback with example: obj = ORTCallback()