class_docstr: Test class simulates the bundle workflow defined by Python script directly.
class_docstr_tok: ["Test","class","simulates","the","bundle","workflow","defined","by","Python","script","directly","."]
code: def __init__(self, filename, output_dir):
      super().__init__(workflow_type="inference")
      self.filename = filename
      self.output_dir = output_dir
      self._bundle_root = "will override"
      self._dataset_dir = "."
      self._device = torch.device("cpu")
      self._data = [{"image": self.filename}]
      self._dataset = None
      self._network_def = None
      self._inferer = None
      self._preprocessing = None
      self._postprocessing = None
      self._evaluator = None
      self._version = None
      self._monai_version = None
      self._pytorch_version = None
      self._numpy_version = None
  
file_path: MONAI/tests/nonconfig_workflow.py
imports: from __future__ import annotations
  import torch
  from monai.bundle import BundleWorkflow
  from monai.data import DataLoader, Dataset
  from monai.engines import SupervisedEvaluator
  from monai.inferers import SlidingWindowInferer
  from monai.networks.nets import UNet
  from monai.transforms import Activationsd, AsDiscreted, Compose, EnsureChannelFirstd, LoadImaged, SaveImaged, ScaleIntensityd
  from monai.utils import BundleProperty, set_determinism
label_desc: Use image node_id 1 to create a new NonConfigWorkflow object from inherited base classes: BundleWorkflow with example: obj = NonConfigWorkflow(filename, output_dir)