code: def _tensor_str(self, indent):
      if self.numel() == 0:
          return "[]"
  
      if self.has_names():
          # There are two main codepaths (possibly more) that tensor printing goes through:
          # - tensor data can fit comfortably on screen
          # - tensor data needs to be summarized
          # Some of the codepaths don't fully support named tensors, so we send in
          # an unnamed tensor to the formatting code as a workaround.
          self = self.rename(None)
  
      summarize = self.numel() > PRINT_OPTS.threshold
  
      if self._is_zerotensor():
          self = self.clone()
  
      # handle the negative bit
      if self.is_neg():
          self = self.resolve_neg()
  
      if self.dtype in [
          torch.float16,
          torch.bfloat16,
          torch.float8_e5m2,
          torch.float8_e5m2fnuz,
          torch.float8_e4m3fn,
          torch.float8_e4m3fnuz,
      ]:
          self = self.float()
  
      if self.dtype is torch.complex32:
          self = self.cfloat()
  
      if self.dtype.is_complex:
          # handle the conjugate bit
          self = self.resolve_conj()
          real_formatter = _Formatter(
              get_summarized_data(self.real) if summarize else self.real
          )
          imag_formatter = _Formatter(
              get_summarized_data(self.imag) if summarize else self.imag
          )
          return _tensor_str_with_formatter(
              self, indent, summarize, real_formatter, imag_formatter
          )
      else:
          formatter = _Formatter(
              get_summarized_data(self) if summarize else self
          )
          return _tensor_str_with_formatter(
              self, indent, summarize, formatter
          )
  
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 8 for calling a global function with example usage: _tensor_str(indent) and returns: str, _tensor_str_with_formatter, _tensor_str_with_formatter