class_docstr: This class wraps an object detection model and performs pre-processing and post-processing.
class_docstr_tok: ["This","class","wraps","an","object","detection","model","and","performs","pre-processing","and","post-processing","."]
docstr: Compile the internal object detection model with :py:func:`torch.compile()`.
docstr_tok: ["Compile","the","internal","object","detection","model","with",":","py",":","func",":","`","torch.compile","(",")","`","."]
code: def compile(
      self,
      *,
      fullgraph: bool = False,
      dynamic: bool = False,
      backend: str = "inductor",
      mode: Optional[str] = None,
      options: Optional[dict[str, str | int | bool]] = None,
      disable: bool = False,
  ) -> None:
      """Compile the internal object detection model with :py:func:`torch.compile()`."""
      self.model = torch.compile(  # type: ignore
          self.model,
          fullgraph=fullgraph,
          dynamic=dynamic,
          backend=backend,
          mode=mode,
          options=options,
          disable=disable,
      )
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from enum import Enum
  from typing import Optional
  import torch
  from kornia.core import Module, Tensor, concatenate
  from kornia.core.check import KORNIA_CHECK_SHAPE
  from kornia.image.base import ImageSize
label_desc: Use image node_id 3 for calling the ObjectDetector obj's underlying member method code with example usage: obj.compile() without return types