code: def load(export_dir, tags, skip_restoring_checkpoint=False):
      """Load a v1-style SavedModel as an object."""
      metrics.IncrementReadApi(_LOAD_V1_V2_LABEL)
      loader = _EagerSavedModelLoader(export_dir)
      result = loader.load(
          tags=tags, skip_restoring_checkpoint=skip_restoring_checkpoint
      )
      metrics.IncrementRead(write_version="1")
      return result
  
imports: import functools
  from tensorflow.python.eager import context
  from tensorflow.python.eager import lift_to_graph
  from tensorflow.python.eager import wrap_function
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.saved_model import function_deserialization
  from tensorflow.python.saved_model import loader_impl
  from tensorflow.python.saved_model import signature_serialization
  from tensorflow.python.saved_model.pywrap_saved_model import metrics
  from tensorflow.python.trackable import asset
  from tensorflow.python.trackable import autotrackable
  from tensorflow.python.trackable import resource
  from tensorflow.python.training import monitored_session
  from tensorflow.python.training import saver
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 for calling a global function with example usage: load(export_dir, tags, skip_restoring_checkpoint) and returns: result