code: def __init__(self, stride=1, pad=0, cover_all=False, **kwargs):
      dilate, groups, cudnn_fast = argument.parse_kwargs(
          kwargs,
          ("dilate", 1),
          ("groups", 1),
          ("cudnn_fast", False),
          deterministic="deterministic argument is not supported anymore. "
          "Use chainer.using_config('cudnn_deterministic', value) context "
          "where value is either `True` or `False`.",
          requires_x_grad="requires_x_grad argument is not supported "
          "anymore. Just remove the argument. Note that whether to compute "
          "the gradient w.r.t. x is automatically decided during "
          "backpropagation.",
      )
  
      self.sy, self.sx = _pair(stride)
      self.ph, self.pw = _pair(pad)
      self.cover_all = cover_all
      self.dy, self.dx = _pair(dilate)
      self.groups = groups
      self.cudnn_fast = cudnn_fast
  
      if self.dx < 1 or self.dy < 1:
          raise ValueError(
              "Dilate should be positive, but {} is "
              "supplied.".format(dilate)
          )
  
imports: import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  import chainer.functions
  from chainer import memory_layouts
  from chainer.utils import argument
  from chainer.utils import conv
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 1 to create a new Convolution2DFunction object from inherited base classes: function_node with example: obj = Convolution2DFunction(stride, pad, cover_all)