code: def try_fake_inference(
      model: tf.keras.Model,
      input_spec: Optional[
          Union[tf.TensorSpec, Sequence[tf.TensorSpec]]
      ],
  ):
      """Try to fake inference for subclassed model to make sure it can be traced/quantized."""
      if hasattr(model, "input_shape") or model.built:
          # Sequential and functional API model has
          # `input_shape` and `output_shape` attributes
          return
      elif input_spec is not None:
          try:
              input_shape = tensor_spec_to_shape(input_spec)
              model.compute_output_shape(input_shape)
              return
          except Exception as _e:
              pass
          inputs = fake_tensor_from_spec(input_spec)
          # `inputs` may be a single Tensor or a list of Tensors
          if isinstance(inputs, list):
              _ = model(*inputs)
          else:
              _ = model(inputs)
          return
      else:
          invalidInputError(
              False,
              "Subclassed model must specify `input_spec` parameter.",
          )
  
imports: import warnings
  from typing import Sequence, Union, Optional
  from bigdl.nano.utils.common import invalidInputError
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: try_fake_inference(model, input_spec) without return types