code: def torch_nn_adapative_pooling_2d(self, input):
      assert input.dim() in [3, 4]
      if isinstance(self.output_size, int):
          output_size = (self.output_size,) * 2
      else:
          output_size = self.output_size
      result_shape = tuple(input.shape[:-2]) + output_size
      return torch.empty(result_shape, device="meta")
  
file_path: ColossalAI/colossalai/fx/tracer/meta_patch/patched_module/pooling.py
imports: import math
  import torch
  from ...registry import meta_patched_module
label_desc: Use image node_id 8 for calling a global function with example usage: torch_nn_adapative_pooling_2d(input) and returns: torch