code: def _result_batchable_to_flat(
      result_batchable, result_flat_signature, batch_size
  ):
      """Converts result_batchable -> result_flat."""
      result_flat = []
      i = 0
      for spec in result_flat_signature:
          # pylint: disable=protected-access
          num_tensors = len(spec._flat_tensor_specs)
          result_flat.append(
              spec._batch(batch_size)._from_compatible_tensor_list(
                  result_batchable[i : i + num_tensors]
              )
          )
          i += num_tensors
      assert i == len(result_batchable)
      return result_flat
  
imports: import re
  from tensorflow.python.autograph.core import ag_ctx
  from tensorflow.python.autograph.impl import api
  from tensorflow.python.eager import context
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import while_loop
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import nest
  from tensorflow.python.util import variable_utils
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling a global function with example usage: _result_batchable_to_flat(result_batchable, result_flat_signature, batch_size) and returns: result_flat