class_docstr: Base class for networks using Tensorforce layers.
code: def __init__(
      self,
      *,
      name,
      inputs_spec,
      device=None,
      l2_regularization=None,
      outputs=None,
  ):
      super().__init__(
          name=name,
          inputs_spec=inputs_spec,
          device=device,
          l2_regularization=l2_regularization,
      )
  
      if self.inputs_spec.is_singleton():
          self.registered_tensors_spec = TensorsSpec(
              state=self.inputs_spec.singleton()
          )
      else:
          self.registered_tensors_spec = self.inputs_spec.copy()
  
      self._output_spec = self.inputs_spec.value()
  
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 LayerbasedNetwork object from inherited base classes: Network with example: obj = LayerbasedNetwork()