code: def map_batch_transform(
      transform: Callable, tapes: QuantumTapeBatch
  ) -> Tuple[QuantumTapeBatch, PostprocessingFn]:
      """Map a transform over multiple tapes.
  
      Args:
          transform (Callable): the transform to be mapped
          tapes (Sequence[QuantumTape]): The sequence of tapes the
              transform should be applied to. Each tape in the sequence
              is transformed by the transform.
  
      **Example**
  
      Consider the following tapes:
  
      .. code-block:: python
  
          H = qml.PauliZ(0) @ qml.PauliZ(1) - qml.PauliX(0)
  
  
          ops1 = [
              qml.RX(0.5, wires=0),
              qml.RY(0.1, wires=1),
              qml.CNOT(wires=(0,1))
          ]
          measurements1 = [qml.expval(H)]
          tape1 = qml.tape.QuantumTape(ops1, measurements1)
  
          ops2 = [qml.Hadamard(0), qml.CRX(0.5, wires=(0,1)), qml.CNOT((0,1))]
          measurements2 = [qml.expval(H + 0.5 * qml.PauliY(0))]
          tape2 = qml.tape.QuantumTape(ops2, measurements2)
  
  
      We can use ``map_batch_transform`` to map a single
      transform across both of the these tapes in such a way
      that allows us to submit a single job for execution:
  
      >>> tapes, fn = map_batch_transform(qml.transforms.hamiltonian_expand, [tape1, tape2])
      >>> dev = qml.device("default.qubit", wires=2)
      >>> fn(qml.execute(tapes, dev, qml.gradients.param_shift))
      [array(0.99500417), array(0.8150893)]
      """
      execution_tapes = []
      batch_fns = []
      tape_counts = []
  
      for t in tapes:
          # Preprocess the tapes by applying transforms
          # to each tape, and storing corresponding tapes
          # for execution, processing functions, and list of tape lengths.
          new_tapes, fn = transform(t)
          execution_tapes.extend(new_tapes)
          batch_fns.append(fn)
          tape_counts.append(len(new_tapes))
  
      def processing_fn(res: ResultBatch) -> ResultBatch:
          """Applies a batch of post-procesing functions to results.
  
          Args:
              res (ResultBatch): the results of executing a batch of circuits
  
          Returns:
              ResultBatch : results that have undergone classical post processing
  
          Closure variables:
              tape_counts: the number of tapes outputted from each application of the transform
              batch_fns: the post processing functions to apply to each sub-batch
  
          """
          count = 0
          final_results = []
  
          for idx, s in enumerate(tape_counts):
              # apply any transform post-processing
              new_res = batch_fns[idx](res[count : count + s])
              final_results.append(new_res)
              count += s
  
          return final_results
  
      return execution_tapes, processing_fn
  
file_path: pennylane/pennylane/transforms/batch_transform.py
imports: import copy
  import functools
  import inspect
  import os
  import types
  import warnings
  from typing import Callable, Tuple
  import pennylane
  from pennylane.typing import ResultBatch
label_desc: Use image node_id 1 for calling a global function with example usage: map_batch_transform(transform, tapes) and returns: execution_tapes,  processing_fn, final_results