class_docstr: Base class for neural networks.
  
  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>.
      inputs_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      outputs (iter[string]): <span style="color:#0000C0"><b>internal use</b></span>.
code: def __init__(
      self,
      *,
      device=None,
      l2_regularization=None,
      name=None,
      inputs_spec=None,
      outputs=None,
  ):
      super().__init__(
          name=name, device=device, l2_regularization=l2_regularization
      )
  
      self.inputs_spec = inputs_spec
  
      if outputs is None:
          self.outputs = outputs
      else:
          self.outputs = tuple(outputs)
          if any(
              not isinstance(output, str) for output in self.outputs
          ):
              raise TensorforceError.value(
                  name="LayerbasedNetwork",
                  argument="outputs",
                  value=self.outputs,
              )
  
imports: from collections import Counter
  import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import ArrayDict, Module, SignatureDict, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util
  from tensorforce.core.layers import Block, Layer, layer_modules, MultiInputLayer, NondeterministicLayer, PreprocessingLayer, Register, Reuse, StatefulLayer, TemporalLayer
  from tensorforce.core.parameters import Parameter
label_desc: Use image node_id 1 to create a new Network object from inherited base classes: Module with example: obj = Network()