code: def build_flash_serve_model_component(
      model, serve_input, output, transform, transform_kwargs
  ):
      # TODO: Resolve this hack
      data_module = DataModule(
          predict_input=serve_input,
          batch_size=1,
          transform=transform,
          transform_kwargs=transform_kwargs,
      )
  
      class MockTrainer(Trainer):
          def __init__(self):
              super().__init__()
              self.state.stage = RunningStage.PREDICTING
  
          @property
          def lightning_module(self):
              return model
  
      data_module.trainer = MockTrainer()
      dataloader = data_module.predict_dataloader()
  
      collate_fn = dataloader.collate_fn
  
      class FlashServeModelComponent(ModelComponent):
          def __init__(self, model):
              self.model = model
              self.model.eval()
              self.serve_input = serve_input
              self.on_after_batch_transfer = (
                  data_module.on_after_batch_transfer
              )
              self.output_transform = (
                  getattr(model, "_output_transform", None)
                  or OutputTransform()
              )
              # TODO (@tchaton) Remove this hack
              self.extra_arguments = (
                  len(
                      inspect.signature(
                          self.model.transfer_batch_to_device
                      ).parameters
                  )
                  == 3
              )
              self.device = self.model.device
  
          @expose(
              inputs={
                  "inputs": FlashInputs(
                      _ServeInputProcessor(serve_input, collate_fn)
                  )
              },
              outputs={"outputs": FlashOutputs(output)},
          )
          def predict(self, inputs):
              with torch.no_grad():
                  if self.extra_arguments:
                      inputs = self.model.transfer_batch_to_device(
                          inputs, self.device, 0
                      )
                  else:
                      inputs = self.model.transfer_batch_to_device(
                          inputs, self.device
                      )
                  inputs = self.on_after_batch_transfer(inputs, 0)
                  preds = self.model.predict_step(inputs, 0)
                  return self.output_transform(preds)
  
      return FlashServeModelComponent(model)
  
imports: import inspect
  from typing import Any, Callable, Mapping
  import torch
  from torch import Tensor
  from flash.core.data.batch import _ServeInputProcessor
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import DataKeys
  from flash.core.data.io.output_transform import OutputTransform
  from flash.core.serve import ModelComponent, expose
  from flash.core.serve.types.base import BaseType
  from flash.core.trainer import Trainer
  from flash.core.utilities.stages import RunningStage
label_desc: Use image node_id 1 for calling a global function with example usage: build_flash_serve_model_component(model, serve_input, output, transform, transform_kwargs) and returns: FlashServeModelComponent, model, self