code: def compute_output_spec(fn, *args, **kwargs):
      def has_none_shape(x):
          """Check for if a `KerasTensor` has dynamic shape."""
          if isinstance(x, KerasTensor):
              return None in x.shape
          return False
  
      def convert_keras_tensor_to_torch(x, fill_value=None):
          """Convert `KerasTensor`s to `torch.Tensor`s."""
          if isinstance(x, KerasTensor):
              shape = list(x.shape)
              if fill_value:
                  for i, e in enumerate(shape):
                      if e is None:
                          shape[i] = fill_value
              return torch.ones(
                  size=shape,
                  dtype=TORCH_DTYPES[x.dtype],
                  device=get_device(),
              )
          return x
  
      def convert_torch_to_keras_tensor(x):
          """Convert `torch.Tensor`s to `KerasTensor`s."""
          if is_tensor(x):
              return KerasTensor(x.shape, standardize_dtype(x.dtype))
          return x
  
      def symbolic_call(fn, args, kwargs, fill_value):
          """Call `fn` to infer output shape and dtype."""
          try:
              # First try instantiating all tensors on the `"meta"` device,
              # which  should give a "zero flop" way to trace shape, but does
              # not have universal support with torch operations.
              with device_scope("meta"):
                  meta_args, meta_kwargs = tree.map_structure(
                      lambda x: convert_keras_tensor_to_torch(
                          x, fill_value
                      ),
                      (args, kwargs),
                  )
                  return fn(*meta_args, **meta_kwargs)
          except:
              with device_scope(DEFAULT_DEVICE):
                  # If the `"meta"` device placement fails, fall back to tracing
                  # eagerly with tensors on the default device. This will be
                  # more robust, but more expensive.
                  eager_args, eager_kwargs = tree.map_structure(
                      lambda x: convert_keras_tensor_to_torch(
                          x, fill_value
                      ),
                      (args, kwargs),
                  )
                  return fn(*eager_args, **eager_kwargs)
  
      with StatelessScope(), torch.no_grad():
          outputs = symbolic_call(fn, args, kwargs, fill_value=83)
  
          none_in_shape = any(
              map(has_none_shape, tree.flatten((args, kwargs)))
          )
          if none_in_shape:
              outputs_1 = outputs
              outputs_2 = symbolic_call(fn, args, kwargs, fill_value=89)
  
              flat_out_1 = tree.flatten(outputs_1)
              flat_out_2 = tree.flatten(outputs_2)
  
              flat_out = []
              for x1, x2 in zip(flat_out_1, flat_out_2):
                  shape = list(x1.shape)
                  for i, e in enumerate(x2.shape):
                      if e != shape[i]:
                          shape[i] = None
                  flat_out.append(
                      KerasTensor(shape, standardize_dtype(x1.dtype))
                  )
              outputs = pack_sequence_as(outputs_1, flat_out)
  
          output_spec = tree.map_structure(
              convert_torch_to_keras_tensor, outputs
          )
      return output_spec
  
imports: import contextlib
  import os
  import numpy
  import torch
  import tree
  from keras.backend.common import KerasVariable
  from keras.backend.common import global_state
  from keras.backend.common import standardize_dtype
  from keras.backend.common.dtypes import result_type
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.backend.config import floatx
  from keras.utils.nest import pack_sequence_as
label_desc: Use image node_id 10 for calling a global function with example usage: compute_output_spec(fn) and returns: output_spec, False, x, x, unknown, torch, KerasTensor, fn, fn