code: def cat(tensors, dim=0):
      def non_empty_tensor(x):
          # special case for cat'ing with an empty tensor -
          # just drop the 'empty' inputs so they don't confuse the logic below.
          return len(x.shape) > 1 or x.shape[0] > 0
  
      filtered_tensors = list(filter(non_empty_tensor, tensors))
  
      if len(filtered_tensors) == 1:
          return filtered_tensors[0].clone()
      elif 1 < len(filtered_tensors) < len(tensors):
          # on the first call, when we remove empty tensors, we redispatch recursively
          return aten.cat.default(filtered_tensors, dim)
      # when no 'filtering' has occurred, we raise to prevent infinite recursion (no more decomposition needed)
      return NotImplemented
  
imports: import functools
  import logging
  import math
  import sys
  import typing
  from typing import Optional
  import torch
  import torch._decomp
  import torch._prims_common
  import torch.ao.quantization.fx._decomposed
  from torch._decomp import core_aten_decompositions, get_decompositions, remove_decompositions
  from torch._decomp.decompositions import _grid_sampler_2d, pw_cast_for_opmath
  from torch._decomp.decompositions_for_rng import extra_random_decomps
  from torch._higher_order_ops.out_dtype import out_dtype
  from torch._prims_common import elementwise_dtypes, ELEMENTWISE_TYPE_PROMOTION_KIND, type_to_dtype
  from .None import config, inductor_prims
label_desc: Use image node_id 14 for calling a global function with example usage: cat(tensors, dim) and returns: NotImplemented, unknown, filtered_tensors, aten