code: def broadcast_expand(
      tape: qml.tape.QuantumTape,
  ) -> (Sequence[qml.tape.QuantumTape], Callable):
      r"""Expand a broadcasted tape into multiple tapes
      and a function that stacks and squeezes the results.
  
      .. warning::
  
          Currently, not all templates have been updated to support broadcasting.
  
      Args:
          tape (QNode or QuantumTape or Callable): Broadcasted tape to be expanded
  
      Returns:
          qnode (QNode) or quantum function (Callable) or tuple[List[QuantumTape], function]:
  
          The transformed circuit as described in :func:`qml.transform <pennylane.transform>`.
  
          - If the input is a QNode, the broadcasted input QNode
            that computes the QNode output serially with multiple circuit evaluations and
            stacks (and squeezes) the results into one batch of results.
  
          - If the input is a tape, a tuple containing a list of generated tapes, together with
            a post-processing function. The number of tapes matches the broadcasting dimension
            of the input tape, and the results from the evaluated tapes are stacked and squeezed
            together in the post-processing function.
  
      This expansion function is used internally whenever a device does not
      support broadcasting.
  
      **Example**
  
      We may use ``broadcast_expand`` on a ``QNode`` to separate it
      into multiple calculations. For this we will provide ``qml.RX`` with
      the ``ndim_params`` attribute that allows the operation to detect
      broadcasting, and set up a simple ``QNode`` with a single operation and
      returned expectation value:
  
      >>> qml.RX.ndim_params = (0,)
      >>> dev = qml.device("default.qubit", wires=1)
      >>> @qml.qnode(dev)
      >>> def circuit(x):
      ...     qml.RX(x, wires=0)
      ...     return qml.expval(qml.PauliZ(0))
  
      We can then call ``broadcast_expand`` on the QNode and store the
      expanded ``QNode``:
  
      >>> expanded_circuit = qml.transforms.broadcast_expand(circuit)
  
      Let's use the expanded QNode and draw it for broadcasted parameters
      with broadcasting axis of length ``3`` passed to ``qml.RX``:
  
      >>> x = pnp.array([0.2, 0.6, 1.0], requires_grad=True)
      >>> print(qml.draw(expanded_circuit)(x))
      0: ──RX(0.20)─┤  <Z>
      0: ──RX(0.60)─┤  <Z>
      0: ──RX(1.00)─┤  <Z>
  
      Executing the expanded ``QNode`` results in three values, corresponding
      to the three parameters in the broadcasted input ``x``:
  
      >>> expanded_circuit(x)
      tensor([0.98006658, 0.82533561, 0.54030231], requires_grad=True)
  
      We also can call the transform manually on a tape:
  
      >>> ops = [qml.RX(pnp.array([0.2, 0.6, 1.0], requires_grad=True), wires=0)]
      >>> measurements = [qml.expval(qml.PauliZ(0))]
      >>> tape = qml.tape.QuantumTape(ops, measurements)
      >>> tapes, fn = qml.transforms.broadcast_expand(tape)
      >>> tapes
      [<QuantumTape: wires=[0], params=1>, <QuantumTape: wires=[0], params=1>, <QuantumTape: wires=[0], params=1>]
      >>> fn(qml.execute(tapes, qml.device("default.qubit", wires=1), None))
      tensor([0.98006658, 0.82533561, 0.54030231], requires_grad=True)
      """
      # pylint: disable=protected-access
      if tape.batch_size is None:
          output_tapes = [tape]
  
          def null_postprocessing(results):
              """A postprocesing function returned by a transform that only converts the batch of results
              into a result for a single ``QuantumTape``.
              """
              return results[0]
  
          processing_fn = null_postprocessing
      else:
          num_tapes = tape.batch_size
          new_ops = _split_operations(tape.operations, num_tapes)
  
          output_tapes = []
          for ops in new_ops:
              new_tape = qml.tape.QuantumScript(
                  ops,
                  tape.measurements,
                  shots=tape.shots,
                  trainable_params=tape.trainable_params,
              )
              output_tapes.append(new_tape)
  
          def processing_fn(
              results: qml.typing.ResultBatch,
          ) -> qml.typing.Result:
              if len(tape.measurements) > 1:
                  processed_results = [
                      qml.math.squeeze(
                          qml.math.stack(
                              [
                                  results[b][i]
                                  for b in range(tape.batch_size)
                              ]
                          )
                      )
                      for i in range(len(tape.measurements))
                  ]
                  return tuple(processed_results)
              return qml.math.squeeze(qml.math.stack(results))
  
      return output_tapes, processing_fn
  
imports: from typing import Sequence, Callable
  import pennylane
  from .core import transform
label_desc: Use image node_id 2 for calling a global function with example usage: broadcast_expand(tape) and returns: output_tapes,  processing_fn, results, qml, tuple