code: def get_summarized_data(self):
      dim = self.dim()
      if dim == 0:
          return self
      if dim == 1:
          if self.size(0) > 2 * PRINT_OPTS.edgeitems:
              return torch.cat(
                  (
                      self[: PRINT_OPTS.edgeitems],
                      self[-PRINT_OPTS.edgeitems :],
                  )
              )
          else:
              return self
      if not PRINT_OPTS.edgeitems:
          return self.new_empty([0] * self.dim())
      elif self.size(0) > 2 * PRINT_OPTS.edgeitems:
          start = [self[i] for i in range(0, PRINT_OPTS.edgeitems)]
          end = [
              self[i]
              for i in range(
                  len(self) - PRINT_OPTS.edgeitems, len(self)
              )
          ]
          return torch.stack(
              [get_summarized_data(x) for x in (start + end)]
          )
      else:
          return torch.stack([get_summarized_data(x) for x in self])
  
imports: import contextlib
  import dataclasses
  import math
  import textwrap
  from typing import Any, Dict, Optional
  import torch
  from torch import inf
label_desc: Use image node_id 10 for calling a global function with example usage: get_summarized_data() and returns: self, self, torch, self, torch, torch