class_docstr: Network consisting of Tensorforce layers (specification key: `custom` or `layered`), which can
  be specified as either a list of layer specifications in the case of a standard sequential
  layer-stack architecture, or as a list of list of layer specifications in the case of a more
  complex architecture consisting of multiple sequential layer-stacks. Note that the final
  action/value layer of the policy/baseline network is implicitly added, so the network output can
  be of arbitrary size and use any activation function, and is only required to be a rank-one
  embedding vector, or optionally have the same shape as the action in the case of a higher-rank
  action shape.
  
  Args:
      layers (iter[specification] | iter[iter[specification]]): Layers configuration, see the
          [layers documentation](../modules/layers.html)
          (<span style="color:#C00000"><b>required</b></span>).
      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,
      layers,
      *,
      device=None,
      l2_regularization=None,
      name=None,
      inputs_spec=None,
      outputs=None,
  ):
      super().__init__(
          device=device,
          l2_regularization=l2_regularization,
          name=name,
          inputs_spec=inputs_spec,
          outputs=outputs,
      )
  
      self.layers = self._parse_layers_spec(
          spec=layers, counter=Counter()
      )
  
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 LayeredNetwork object from inherited base classes: LayerbasedNetwork with example: obj = LayeredNetwork(layers)