code: def from_markdown_table(data: str) -> TimingResultType:
      out = data.strip().split("\n")
      out = out[2:]  # Ignore the header lines
  
      res: TimingResultType
      res = defaultdict(defaultdict)
  
      for line in out:
          model, task, mean, var = (
              f.strip() for f in line.strip().split("|") if f
          )
          res[model][task] = (float(mean), float(var))
  
      return res
  
file_path: pytorch/benchmarks/functional_autograd_benchmark/utils.py
imports: from collections import defaultdict
  from typing import Callable, Dict, List, Tuple, Union
  import torch
  from torch import nn, Tensor
label_desc: Use image node_id 6 for calling a global function with example usage: from_markdown_table(data) and returns: res