code: def pytreeify(cls):
      """Pytrees refer to a tree-like structure built out of container-like Python objects. The pytreeify class is used
      to bypass some PyTorch limitation of `autograd.Function`. The forward pass can only return tuple of tensors but
      not any other nested structure. This class apply flatten to the forward pass and unflatten the results in the
      apply function. In this way, it is possible to treat multiple tapes with multiple measurements.
      """
      orig_fw = cls.forward
      orig_bw = cls.backward
      orig_apply = cls.apply
  
      def new_apply(*inp):
          # Inputs already flat
          out_struct_holder = []
          flat_out = orig_apply(out_struct_holder, *inp)
          return pytree.tree_unflatten(flat_out, out_struct_holder[0])
  
      def new_forward(ctx, out_struct_holder, *inp):
          out = orig_fw(ctx, *inp)
          flat_out, out_struct = pytree.tree_flatten(out)
          ctx._out_struct = out_struct
          out_struct_holder.append(out_struct)
          return tuple(flat_out)
  
      def new_backward(ctx, *flat_grad_outputs):
          grad_outputs = pytree.tree_unflatten(
              flat_grad_outputs, ctx._out_struct
          )
          grad_inputs = orig_bw(ctx, *grad_outputs)
          # None corresponds to the diff of out_struct_holder
          return (None,) + tuple(grad_inputs)
  
      cls.apply = new_apply
      cls.forward = new_forward
      cls.backward = new_backward
      return cls
  
file_path: pennylane/pennylane/workflow/interfaces/torch.py
imports: import inspect
  import logging
  import numpy
  import torch
  import torch.utils._pytree
  import pennylane
label_desc: Use image node_id 1 for calling a global function with example usage: pytreeify(cls) and returns: cls, pytree, tuple, unknown