code: def _tensor_str_with_formatter(
      self, indent, summarize, formatter1, formatter2=None
  ):
      dim = self.dim()
  
      if dim == 0:
          return _scalar_str(self, formatter1, formatter2)
  
      if dim == 1:
          return _vector_str(
              self, indent, summarize, formatter1, formatter2
          )
  
      if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:
          slices = (
              [
                  _tensor_str_with_formatter(
                      self[i],
                      indent + 1,
                      summarize,
                      formatter1,
                      formatter2,
                  )
                  for i in range(0, PRINT_OPTS.edgeitems)
              ]
              + ["..."]
              + [
                  _tensor_str_with_formatter(
                      self[i],
                      indent + 1,
                      summarize,
                      formatter1,
                      formatter2,
                  )
                  for i in range(
                      len(self) - PRINT_OPTS.edgeitems, len(self)
                  )
              ]
          )
      else:
          slices = [
              _tensor_str_with_formatter(
                  self[i], indent + 1, summarize, formatter1, formatter2
              )
              for i in range(0, self.size(0))
          ]
  
      tensor_str = ("," + "\n" * (dim - 1) + " " * (indent + 1)).join(
          slices
      )
      return "[" + tensor_str + "]"
  
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 7 for calling a global function with example usage: _tensor_str_with_formatter(indent, summarize, formatter1, formatter2) and returns: unknown, _scalar_str, _vector_str