code: def remove_barrier(
      tape: QuantumTape,
  ) -> (Sequence[QuantumTape], Callable):
      """Quantum transform to remove Barrier gates.
  
      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**
  
      The transform can be applied on :class:`QNode` directly.
  
      .. code-block:: python
  
          @remove_barrier
          @qml.qnode(device=dev)
          def circuit(x, y):
              qml.Hadamard(wires=0)
              qml.Hadamard(wires=1)
              qml.Barrier(wires=[0,1])
              qml.PauliX(wires=0)
              return qml.expval(qml.PauliZ(0))
  
      The barrier is then removed before execution.
  
      .. details::
          :title: Usage Details
  
          Consider the following quantum function:
  
          .. code-block:: python
  
              def qfunc(x, y):
                  qml.Hadamard(wires=0)
                  qml.Hadamard(wires=1)
                  qml.Barrier(wires=[0,1])
                  qml.PauliX(wires=0)
                  return qml.expval(qml.PauliZ(0))
  
          The circuit before optimization:
  
          >>> dev = qml.device('default.qubit', wires=2)
          >>> qnode = qml.QNode(qfunc, dev)
          >>> print(qml.draw(qnode)(1, 2))
              0: ──H──╭||──X──┤ ⟨Z⟩
              1: ──H──╰||─────┤
  
  
          We can remove the Barrier by running the ``remove_barrier`` transform:
  
          >>> optimized_qfunc = remove_barrier(qfunc)
          >>> optimized_qnode = qml.QNode(optimized_qfunc, dev)
          >>> print(qml.draw(optimized_qnode)(1, 2))
             0: ──H──X──┤ ⟨Z⟩
             1: ──H─────┤
  
      """
      operations = filter(
          lambda op: op.name != "Barrier", tape.operations
      )
      new_tape = type(tape)(
          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]  # pragma: no cover
  
      return [new_tape], null_postprocessing
  
label_desc: Use image node_id 1 for calling a global function with example usage: remove_barrier(tape) and returns: list,  null_postprocessing, results