code: def nt_tree_fn(
      nargs: Optional[int] = None,
      tree_structure_argnum: Optional[int] = None,
      reduce: Callable = lambda x: x,
  ):
      """Convert a function that acts on single inputs to one that acts on trees.
  
      `nt_tree_fn` treats the first `nargs` arguments as NTTrees and the remaining
      arguments as broadcasted over the tree structure. `nt_tree_fn` then calls the
      function on each leaf of the tree. Each node of the tree optionally calls a
      reduce function over the values of its children.
  
      If `tree_structure_argnum` is None then each of the NTTrees must have the same
      structure. If `tree_structure_argnum` is an integer then a specific tree is
      used to infer the structure.
  
      Args:
        nargs:
          The number of arguments to be treated as NTTrees. If `nargs` is `None`
          then all the arguments are used. `nargs` can also be negative which
          follows numpy's semantics for array indexing.
  
        tree_structure_argnum:
          The argument used to infer the tree structure to be traversed. If
          `tree_structure_argnum` is None then a check is performed to ensure that
          all trees have the same structure.
  
        reduce:
          A callable that is applied recursively by each internal tree node to its
          children.
  
      Returns:
        A decorator `tree_fn` that transforms a function, `fn`, from acting on
        leaves to acting on NTTrees.
      """
  
      def check_tree_structure(args):
          """Ensure the structure of the trees in each of the `nargs` is the same."""
          if any(is_list_or_tuple(x) for x in args):
              if not all(type(x) == type(args[0]) for x in args[1:]):
                  raise TypeError(
                      f"Inconsistent NTTree structure found. "
                      f"Node Types: {[type(x) for x in args]}."
                  )
  
              """
          Regarding the use of zip, consider an example `x1 = x2 = (1, (1, 1))`.
          We would like to determine whether these two trees have the same
          structure.
  
          On the first recurrence `x1` and `x2` are both tuples so the check
          passes and `zip(*args) = [(1, 1), ((1, 1), (1, 1))]` so that
          `(check_tree_structure(x) for x in zip(x1, x2))` will first check that
          the first element of `x1` has the same tree structure as the first
          element of `x2` and then the second element and so on.
        """
              for x in zip(*args):
                  check_tree_structure(x)
  
      def tree_fn(fn):
          @wraps(fn)
          def wrapped_fn(*args, **kwargs):
              _nargs = len(args) if nargs is None else nargs
              recurse, norecurse = args[:_nargs], args[_nargs:]
  
              structure_argnum = tree_structure_argnum
              if structure_argnum is None:
                  check_tree_structure(recurse)
                  structure_argnum = 0
  
              if is_list_or_tuple(args[structure_argnum]):
                  list_or_tuple = type(args[structure_argnum])
                  return reduce(
                      list_or_tuple(
                          wrapped_fn(*(xs + norecurse), **kwargs)
                          for xs in zip(*recurse)
                      )
                  )
              return fn(*args, **kwargs)
  
          return wrapped_fn
  
      return tree_fn
  
imports: from collections import namedtuple
  import functools
  import inspect
  import operator
  import types
  from typing import Any, Callable, Iterable, Optional, Sequence, Sized, TypeVar, Union
  import warnings
  import jax
  from jax import core
  from jax import random
  import jax.numpy
  from jax.tree_util import tree_all
  from jax.tree_util import tree_map
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: nt_tree_fn(nargs, tree_structure_argnum, reduce) and returns: tree_fn, wrapped_fn, fn, reduce