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 __init__(self, *, device=None, l2_regularization=None, name=None):
      name = name.replace("/", "_")
      super().__init__(name=name)
  
      self.checkpoint = None
      self.is_trainable = None
      self.is_saved = None
      self.is_initialized = None
  
      assert len(Module._MODULE_STACK) >= 1
      if isinstance(Module._MODULE_STACK[-1], type):
          assert isinstance(self, Module._MODULE_STACK[-1])
      else:
          # Not always type, e.g. tf_optimizer uses functools.partial
          assert isinstance(
              Module._MODULE_STACK[-1], functools.partial
          ) and isinstance(self, Module._MODULE_STACK[-1].func)
      Module._MODULE_STACK[-1] = self
      if len(Module._MODULE_STACK) > 1:
          self.parent = Module._MODULE_STACK[-2]
      else:
          self.parent = None
  
      # Device
      if device is None:
          self.device = util.NullContext()
      else:
          self.device = tf.device(device_name=device)
  
      # L2 regularization
      if l2_regularization is None:
          self.l2_regularization = None
      else:
          self.l2_regularization = self.submodule(
              name="l2_regularization",
              module=l2_regularization,
              modules=tensorforce.core.parameter_modules,
              is_trainable=False,
              dtype="float",
              min_value=0.0,
          )
  
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 1 to create a new Module object from inherited base classes: tf with example: obj = Module()