class_docstr: Base class for modules.
  
  Args:
      device (string): Device name
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      l2_regularization (float >= 0.0): Scalar controlling L2 regularization
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
code: def restore(self, *, directory, filename=None):
      if filename is None:
          filename = self.full_name.replace("/", ".")
      if self.checkpoint is None:
          self.checkpoint = tf.train.Checkpoint(**{self.name: self})
      try:
          self.checkpoint.restore(
              save_path=os.path.join(directory, filename)
          ).expect_partial()
      except AssertionError as exc:
          if len(exc.args) != 1 or not re.match(
              pattern=r"Some Python objects were not bound to checkpointed values, likely due to "
              r"changes in the Python program: \[<tf\.Variable 'save_counter:0' "
              r"shape=\(\) dtype=int64, numpy=[0-9]*>(, <tf\.Variable 'save_counter:0' "
              r"shape=\(\) dtype=int64, numpy=[0-9]*>)*\]",
              string=exc.args[0],
          ):
              raise exc
  
imports: import functools
  import json
  import os
  import re
  import numpy
  import tensorflow
  from tensorforce import TensorforceError, util
  import tensorforce.core
  from tensorforce.core import SignatureDict, TensorSpec, tf_util, VariableDict
label_desc: Use image node_id 14 for calling the Module obj's underlying member method code with example usage: obj.restore() without return types