class_docstr: Example of weight sharing between two different TFModelV2s.
  
  NOTE: This will only work for tf1 (static graph). When running with
  config.framework_str=tf2, use TF2SharedWeightsModel, instead!
  
  Here, we share the variables defined in the 'shared' variable scope
  by entering it explicitly with tf1.AUTO_REUSE. This creates the
  variables for the 'fc1' layer in a global scope called 'shared'
  (outside of the Policy's normal variable scope).
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code: def forward(self, input_dict, state, seq_lens):
      out, self._value_out = self.base_model(input_dict["obs"])
      return out, []
  
imports: import numpy
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
label_desc: Use image node_id 2 for calling the SharedWeightsModel1 obj's underlying member method code with example usage: obj.forward(input_dict, state, seq_lens) and returns: out,  list