code: def __init__(
      self, case, mode, device, dtype, kernel_size, N, C, H, W
  ):
      super().__init__(mode, device)
      self.case = case
      self.kernel_size = kernel_size
      self.N = N
      self.C = C
      self.H = H
      self.W = W
      self.data = self.rand(
          [N, C, H, W],
          device=device,
          dtype=dtype,
          requires_grad=self.requires_grad,
      )
  
file_path: pytorch/benchmarks/tensorexpr/pooling.py
imports: from .None import benchmark
label_desc: Use image node_id 1 to create a new PoolingBench object from inherited base classes: benchmark with example: obj = PoolingBench(case, mode, device, dtype, kernel_size, N, C, H, W)