code: def meta__adaptive_avg_pool2d_backward(grad_out, self):
      ndim = grad_out.ndim
      for i in range(1, ndim):
          check(
              grad_out.size(i) > 0,
              lambda: f"adaptive_avg_pool2d_backward(): Expected grad_output to have non-zero \
                        size for non-batch dimensions, {grad_out.shape} with dimension {i} being empty",
          )
      check(
          ndim == 3 or ndim == 4,
          lambda: f"adaptive_avg_pool2d_backward(): Expected 3D or 4D tensor, but got {self.shape}",
      )
      check(
          self.dtype == grad_out.dtype,
          lambda: f"expected dtype {self.dtype} for `grad_output` but got dtype {grad_out.dtype}",
      )
      return self.new_empty(self.shape)
  
imports: import torch
  from torch._meta_registrations import register_meta
  from torch._prims_common import check
label_desc: Use image node_id 2 for calling a global function with example usage: meta__adaptive_avg_pool2d_backward(grad_out, ) and returns: self