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 track(self, *, label, name, data):
      # label
      if not isinstance(label, str):
          raise TensorforceError.type(
              name="Module.track", argument="label", dtype=type(label)
          )
      # name
      if not isinstance(name, str):
          raise TensorforceError.type(
              name="Module.track", argument="name", dtype=type(name)
          )
      # data
      if not tf_util.is_tensor(x=data) and not callable(data):
          raise TensorforceError.type(
              name="Module.track", argument="data", dtype=type(data)
          )
  
      if self.root.tracking == "all" or label in self.root.tracking:
          if name not in self.tracking_variables:
              raise TensorforceError.value(
                  name="Module.track",
                  argument="name",
                  value=name,
                  hint="is not registered",
              )
  
          if callable(data):
              value = data()
          else:
              value = data
          assignment = self.tracking_variables[name].assign(value=value)
          return [assignment]
  
      else:
          return list()
  
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 24 for calling the Module obj's underlying member method code with example usage: obj.track() and returns: list, list