docstr: Test function without test time augmentation.
  
  Args:
      inputs (list[paddle.Tensor]): List of multiple images.
      img_metas (list[dict]): List of image information.
      rescale (bool, optional): Whether to rescale the results.
          Defaults to False.
  
  Returns:
      list[list[np.ndarray]]: BBox and keypoint results of each image
          and classes. The outer list corresponds to each image.
          The inner list corresponds to each class.
docstr_tok: ["Test","function","without","test","time","augmentation",".","Args",":","inputs","(","list","[","paddle.Tensor","]",")",":","List","of","multiple","images",".","img_metas","(","list","[","dict","]",")",":","List","of","image","information",".","rescale","(","bool",",","optional",")",":","Whether","to","rescale","the","results",".","Defaults","to","False",".","Returns",":","list","[","list","[","np.ndarray","]","]",":","BBox","and","keypoint","results","of","each","image","and","classes",".","The","outer","list","corresponds","to","each","image",".","The","inner","list","corresponds","to","each","class","."]
code: def simple_test(self, inputs, img_metas, rescale=False):
      """Test function without test time augmentation.
  
      Args:
          inputs (list[paddle.Tensor]): List of multiple images.
          img_metas (list[dict]): List of image information.
          rescale (bool, optional): Whether to rescale the results.
              Defaults to False.
  
      Returns:
          list[list[np.ndarray]]: BBox and keypoint results of each image
              and classes. The outer list corresponds to each image.
              The inner list corresponds to each class.
      """
      batch_size = len(img_metas)
      assert batch_size == 1, (
          "Currently only batch_size 1 for inference "
          f"mode is supported. Found batch_size {batch_size}."
      )
      feat = self.extract_feat(inputs)
      results_list = self.bbox_head.simple_test(
          feat, img_metas, rescale=rescale
      )
  
      bbox_kpt_results = [
          self.bbox_kpt2result(
              det_bboxes,
              det_labels,
              det_kpts,
              self.bbox_head.num_classes,
          )
          for det_bboxes, det_labels, det_kpts in results_list
      ]
      return bbox_kpt_results
  
file_path: PaddleDetection/ppdet/modeling/architectures/keypoint_petr.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from ppdet.core.workspace import register
  from .meta_arch import BaseArch
  from ..None import layers
label_desc: Use image node_id 7 for calling the PETR obj's underlying member method code with example usage: obj.simple_test(inputs, img_metas, rescale) and returns: bbox_kpt_results