class_docstr: The signature of this ``torch.autograd.Function`` is designed to
  work around Torch restrictions.
  
  In particular, ``torch.autograd.Function``:
  
  - Cannot accept keyword arguments. As a result, we pass a dictionary
    as the first argument ``kwargs``. This dictionary **must** contain:
  
    * ``"tapes"``: the quantum tapes to batch evaluate
    * ``"execute_fn"``: a function that calculates the results of the tapes
    * ``"jpc"``: a :class:`~.JacobianProductCalculator` that can compute the vjp.
  
  Further, note that the ``parameters`` argument is dependent on the
  ``tapes``; this function should always be called
  with the parameters extracted directly from the tapes as follows:
  
  >>> parameters = [p for t in tapes for p in t.get_parameters()]
  >>> kwargs = {"tapes": tapes, "execute_fn": execute_fn, "jpc": jpc}
  >>> ExecuteTapes.apply(kwargs, *parameters)
class_docstr_tok: ["The","signature","of","this","``","torch.autograd.Function","``","is","designed","to","work","around","Torch","restrictions",".","In","particular",",","``","torch.autograd.Function","``",":","-","Can","not","accept","keyword","arguments",".","As","a","result",",","we","pass","a","dictionary","as","the","first","argument","``","kwargs","``",".","This","dictionary","*","*","must","*","*","contain",":","*","``","``","tapes","''","``",":","the","quantum","tapes","to","batch","evaluate","*","``","``","execute_fn","''","``",":","a","function","that","calculates","the","results","of","the","tapes","*","``","``","jpc","''","``",":","a",":","class",":","`","~.JacobianProductCalculator","`","that","can","compute","the","vjp",".","Further",",","note","that","the","``","parameters","``","argument","is","dependent","on","the","``","tapes","``",";","this","function","should","always","be","called","with","the","parameters","extracted","directly","from","the","tapes","as","follows",":",">",">",">","parameters","=","[","p","for","t","in","tapes","for","p","in","t.get_parameters","(",")","]",">",">",">","kwargs","=","{","``","tapes","''",":","tapes",",","``","execute_fn","''",":","execute_fn",",","``","jpc","''",":","jpc","}",">",">",">","ExecuteTapes.apply","(","kwargs",",","*","parameters",")"]
docstr: Implements the forward pass batch tape evaluation.
docstr_tok: ["Implements","the","forward","pass","batch","tape","evaluation","."]
code: def forward(
      ctx, kwargs, *parameters
  ):  # pylint: disable=arguments-differ
      """Implements the forward pass batch tape evaluation."""
      if logger.isEnabledFor(logging.DEBUG):
          logger.debug(
              "Entry with args=(ctx=%s, kwargs=%s, parameters=%s) called by=%s",
              ctx,
              kwargs,
              parameters,
              "::L".join(
                  str(i)
                  for i in inspect.getouterframes(
                      inspect.currentframe(), 2
                  )[1][1:3]
              ),
          )
  
      ctx.tapes = kwargs["tapes"]
      ctx.jpc = kwargs["jpc"]
  
      res = tuple(kwargs["execute_fn"](ctx.tapes))
  
      # if any input tensor uses the GPU, the output should as well
      ctx.torch_device = None
  
      for p in parameters:
          if (
              isinstance(p, torch.Tensor) and p.is_cuda
          ):  # pragma: no cover
              ctx.torch_device = p.get_device()
              break
      res = tuple(_res_to_torch(r, ctx) for r in res)
      return res
  
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 the ExecuteTapes obj's underlying member method code with example usage: obj.forward(ctx, kwargs) and returns: res