class_docstr: A subclass of ``BoxMode``.
  
  Also represented as "xyxy" or "xyzxyz", with format of
  [xmin, ymin, xmax, ymax] or [xmin, ymin, zmin, xmax, ymax, zmax].
  
  Example:
      .. code-block:: python
  
          CornerCornerModeTypeA.get_name(spatial_dims=2) # will return "xyxy"
          CornerCornerModeTypeA.get_name(spatial_dims=3) # will return "xyzxyz"
class_docstr_tok: ["A","subclass","of","``","BoxMode","``",".","Also","represented","as","``","xyxy","''","or","``","xyzxyz","''",",","with","format","of","[","xmin",",","ymin",",","xmax",",","ymax","]","or","[","xmin",",","ymin",",","zmin",",","xmax",",","ymax",",","zmax","]",".","Example",":","..","code-block",":",":","python","CornerCornerModeTypeA.get_name","(","spatial_dims=2",")","#","will","return","``","xyxy","''","CornerCornerModeTypeA.get_name","(","spatial_dims=3",")","#","will","return","``","xyzxyz","''"]
code: def corners_to_boxes(self, corners: Sequence) -> torch.Tensor:
      boxes: torch.Tensor
      boxes = torch.cat(tuple(corners), dim=-1)
      return boxes
  
imports: from __future__ import annotations
  import inspect
  import warnings
  from abc import ABC, abstractmethod
  from collections.abc import Callable, Sequence
  from copy import deepcopy
  import numpy
  import torch
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
  from monai.utils import look_up_option
  from monai.utils.enums import BoxModeName
  from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
label_desc: Use image node_id 2 for calling the CornerCornerModeTypeA obj's underlying member method code with example usage: obj.corners_to_boxes(corners) and returns: boxes