code: def _postprocess_flat_outputs(outputs):
      """Validates flat outputs and adds back device assignments.
  
      Args:
        outputs: Output from `computation` inside `xla.compile`.
  
      Returns:
        Tensors and Operations extracted from outputs.
      """
      # Following code segment is to preserve legacy behavior. Previously we only
      # supported flat outputs and thus for consistency it was nice to convert even
      # single element into a tuple. But now that we support arbitrary output
      # structure, this is no longer necessary.
      # TODO(b/121383831): Migrate all legacy use cases and delete this special
      # case.
      # If the computation returns `None`, make it an empty tuple.
      if outputs is None:
          outputs = tuple()
      # If the computation only returned one value, make it a tuple.
      if not isinstance(outputs, collections_abc.Sequence):
          outputs = (outputs,)
  
      # Append `no_op` here so that return value of this function always contains
      # at least one op that can trigger XlaLaunch node.
      outputs += (control_flow_ops.no_op(),)
      try:
          outputs = [
              o
              if isinstance(o, ops.Operation)
              else ops.convert_to_tensor(o)
              for o in outputs
          ]
      except Exception as e:
          raise ValueError(
              "XLA computation function return values must all either be Operations"
              ' or convertible to Tensors. Got error: "%s"' % str(e)
          )
  
      # Separates the returned Operations and Tensors.
      output_operations = [
          o for o in outputs if isinstance(o, ops.Operation)
      ]
      output_tensors = [
          o for o in outputs if not isinstance(o, ops.Operation)
      ]
  
      if outputs != output_tensors + output_operations:
          raise ValueError(
              "XLA computation function must return zero or more Tensor values "
              "followed by zero or more Operations."
          )
  
      new_output_tensors = []
      for t in output_tensors:
          with ops.device(t.device if t.device else ""):
              new_output_tensors.append(array_ops.identity(t))
  
      return new_output_tensors, output_operations
  
imports: import contextlib
  from tensorflow.compiler.jit.ops import xla_ops
  from tensorflow.compiler.jit.ops import xla_ops_grad
  from tensorflow.core.framework import attr_value_pb2
  from tensorflow.python.distribute import summary_op_util
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import compat
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_inspect
  from tensorflow.python.util.compat import collections_abc
  from tensorflow.python.util.deprecation import deprecated
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling a global function with example usage: _postprocess_flat_outputs(outputs) and returns: new_output_tensors,  output_operations