code: def merge_amplitude_embedding(
      tape: QuantumTape,
  ) -> (Sequence[QuantumTape], Callable):
      r"""Quantum function transform to combine amplitude embedding templates that act on different qubits.
  
      Args:
          tape (QNode or QuantumTape or Callable): A quantum circuit.
  
      Returns:
          qnode (QNode) or quantum function (Callable) or tuple[List[.QuantumTape], function]: The transformed circuit as described in :func:`qml.transform <pennylane.transform>`.
  
  
      **Example**
  
      >>> dev = qml.device('default.qubit', wires=4)
  
      You can apply the transform directly on :class:`QNode`:
  
      .. code-block:: python
  
          @qml.transforms.merge_amplitude_embedding
          @qml.qnode(device=dev)
          def circuit():
              qml.CNOT(wires = [0,1])
              qml.AmplitudeEmbedding([0,1], wires = 2)
              qml.AmplitudeEmbedding([0,1], wires = 3)
              return qml.state()
  
      >>> circuit()
      [1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
  
      .. details::
          :title: Usage Details
  
          You can also apply it on quantum function.
  
          .. code-block:: python
  
              def qfunc():
                  qml.CNOT(wires = [0,1])
                  qml.AmplitudeEmbedding([0,1], wires = 2)
                  qml.AmplitudeEmbedding([0,1], wires = 3)
                  return qml.state()
  
          The circuit before compilation will not work because of using two amplitude embedding.
  
          Using the transformation we can join the different amplitude embedding into a single one:
  
          >>> optimized_qfunc = qml.transforms.merge_amplitude_embedding(qfunc)
          >>> optimized_qnode = qml.QNode(optimized_qfunc, dev)
          >>> print(qml.draw(optimized_qnode)())
          0: ─╭●──────────────────────┤  State
          1: ─╰X──────────────────────┤  State
          2: ─╭AmplitudeEmbedding(M0)─┤  State
          3: ─╰AmplitudeEmbedding(M0)─┤  State
          M0 =
          [0.+0.j 0.+0.j 0.+0.j 1.+0.j]
  
      """
      new_operations = []
      visited_wires = set()
      input_wires, input_vectors, input_batch_size = [], [], []
      for current_gate in tape.operations:
          wires_set = set(current_gate.wires)
  
          # Check if the current gate is an AmplitudeEmbedding.
          if not isinstance(current_gate, AmplitudeEmbedding):
              new_operations.append(current_gate)
              visited_wires = visited_wires.union(wires_set)
              continue
  
          # Check the qubits have not been used.
          if len(visited_wires.intersection(wires_set)) > 0:
              raise DeviceError(
                  f"Operation {current_gate.name} cannot be used after other Operation applied in the same qubit "
              )
          input_wires.append(current_gate.wires)
          input_vectors.append(current_gate.parameters[0])
          input_batch_size.append(current_gate.batch_size)
          visited_wires = visited_wires.union(wires_set)
  
      if len(input_wires) > 0:
          final_wires = input_wires[0]
          final_vector = input_vectors[0]
          final_batch_size = input_batch_size[0]
  
          # Merge all parameters and qubits into a single one.
          for w, v, b in zip(
              input_wires[1:], input_vectors[1:], input_batch_size[1:]
          ):
              final_vector = (
                  final_vector[..., :, None] * v[..., None, :]
              )
              final_batch_size = final_batch_size or b
              final_wires = final_wires + w
  
              if final_batch_size:
                  final_vector = reshape(
                      final_vector, (final_batch_size, -1)
                  )
              else:
                  final_vector = flatten(final_vector)
  
          with QueuingManager.stop_recording():
              new_operations.insert(
                  0, AmplitudeEmbedding(final_vector, wires=final_wires)
              )
  
      new_tape = type(tape)(
          new_operations, tape.measurements, shots=tape.shots
      )
  
      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]
  
      return [new_tape], null_postprocessing
  
imports: from typing import Sequence, Callable
  from pennylane.transforms import transform
  from pennylane.tape import QuantumTape
  from pennylane import AmplitudeEmbedding
  from pennylane._device import DeviceError
  from pennylane.math import flatten, reshape
  from pennylane.queuing import QueuingManager
label_desc: Use image node_id 1 for calling a global function with example usage: merge_amplitude_embedding(tape) and returns: list,  null_postprocessing, results