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: Detect objects in a given list of images.
  
  Args:
      images: list of RGB images. Each image is a Tensor with shape :math:`(3, H, W)`.
  
  Returns:
      list of detections found in each image. For item in a batch, shape is :math:`(D, 6)`, where :math:`D` is the
      number of detections in the given image, :math:`6` represents class id, score, and `xywh` bounding box.
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code: def forward(self, images: list[Tensor]) -> list[Tensor]:
      """Detect objects in a given list of images.
  
      Args:
          images: list of RGB images. Each image is a Tensor with shape :math:`(3, H, W)`.
  
      Returns:
          list of detections found in each image. For item in a batch, shape is :math:`(D, 6)`, where :math:`D` is the
          number of detections in the given image, :math:`6` represents class id, score, and `xywh` bounding box.
      """
      images, images_sizes = self.pre_processor(images)
      logits, boxes = self.model(images)
      detections = self.post_processor(logits, boxes, images_sizes)
      return detections
  
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 2 for calling the ObjectDetector obj's underlying member method code with example usage: obj.forward(images) and returns: detections