code: def _prep_data_for_feed_input(
      data, batch_size, layout, device_id=None
  ):
      def to_numpy(x):
          if _types._is_mxnet_array(x):
              return x.asnumpy()
          elif _types._is_torch_tensor(x):
              return x.numpy()
          else:
              return x
  
      # __cuda_array_interface__ doesn't provide any way to pass the information about the device
      # where the memory is located. It is assumed that the current device is the one that
      # the memory belongs to, unless the user sets the device explicitly
      # creating TensorGPU/TensorListGPU
      if isinstance(
          data, (_tensors.TensorListCPU, _tensors.TensorListGPU)
      ):
          if layout is not None:
              _check_data_batch(data, batch_size, layout)
              data = type(data)(data, layout)
      elif isinstance(data, list):
          inputs = []
          checked = False
          for datum in data:
              (is_dlpack, is_gpu_data) = _b.CheckDLPackCapsule(datum)
              if not is_dlpack and not checked:
                  _check_data_batch(data, batch_size, layout)
                  checked = True
              if isinstance(
                  datum, (_tensors.TensorCPU, _tensors.TensorGPU)
              ):
                  inp = (
                      type(datum)(datum, layout=layout)
                      if layout is not None
                      else datum
                  )
              elif is_dlpack:
                  if is_gpu_data:
                      inp = _tensors.TensorGPU(datum, layout or "")
                  else:
                      inp = _tensors.TensorCPU(datum, layout or "")
              elif hasattr(datum, "__cuda_array_interface__"):
                  array_device_id = _types._get_device_id_for_array(
                      datum
                  )
                  if array_device_id is None:
                      array_device_id = device_id
                  inp = _tensors.TensorGPU(
                      datum, layout or "", array_device_id
                  )
              else:
                  datum = to_numpy(datum)
                  inp = _tensors.TensorCPU(datum, layout or "")
              inputs.append(inp)
          assert all(
              isinstance(inp, type(inputs[0])) for inp in inputs
          ), "Mixed input types are not support, all need to reside on the CPU or GPU"
          data = inputs
      else:
          (is_dlpack, is_gpu_data) = _b.CheckDLPackCapsule(data)
          if not is_dlpack:
              _check_data_batch(data, batch_size, layout)
          if hasattr(data, "__cuda_array_interface__"):
              array_device_id = _types._get_device_id_for_array(data)
              if array_device_id is None:
                  array_device_id = device_id
              data = _tensors.TensorListGPU(
                  data, layout or "", array_device_id
              )
          elif is_dlpack:
              if is_gpu_data:
                  data = _tensors.TensorListGPU(data, layout or "")
              else:
                  data = _tensors.TensorListCPU(data, layout or "")
          else:
              data = to_numpy(data)
              data = _tensors.TensorListCPU(data, layout or "")
      return data
  
imports: from nvidia.dali import backend
  from nvidia.dali import tensors
  from nvidia.dali import types
  from nvidia.dali._multiproc.messages import TaskArgs, SampleRange
  import nvidia.dali.types
  from nvidia.dali._utils.external_source_impl import get_callback_from_source, accepted_arg_count, SourceKind
label_desc: Use image node_id 3 for calling a global function with example usage: _prep_data_for_feed_input(data, batch_size, layout, device_id) and returns: data, x, x, x