code: def may_depend_on(a: Node, b: Node, search_depth: int = 6):
      """
      Determine if one node depends on another in a torch.fx.Graph.
  
      Arguments:
          a: The node that may have a dependency on b.
          b: The node that a may have a dependency on.
          search_depth: In the case of an indirect dependency, this function
                          searches upto this many nodes away in search of a
                          data dependency. If none is found, the function
                          makes the conservative assumption that there is a
                          dependency.
  
      Returns:
          True if a may depend on b, False if it definitely does not.
      """
      # Equivalence is defined as dependence.
      if a == b:
          return True
  
      # If a has no inputs, it cannot depend on b.
      if len(a.all_input_nodes) == 0:
          return False
  
      # If the search depth has been exhausted and no conclusion has been
      # reached, assume that there is a data dependency.
      if search_depth == 0:
          return True
  
      # Recursively check all inputs of a.
      for inp in a.all_input_nodes:
          if may_depend_on(inp, b, search_depth - 1):
              return True
  
      return False
  
file_path: pytorch/torch/fx/experimental/merge_matmul.py
imports: import torch
  from torch.fx.node import Node
  from torch.fx._symbolic_trace import symbolic_trace
  from torch.fx.passes.tools_common import legalize_graph
  import itertools
  import operator
  from typing import Dict, List, Tuple
label_desc: Use image node_id 2 for calling a global function with example usage: may_depend_on(a, b, search_depth) and returns: False, True, False, True, True