class_docstr: Model class for TensorFlow.
  
  A TfModel only contains the structure/configuration of the underlying
  computation graphs. Connectivity information are all in Network class.
  A TfModel contains zero or more Network.
  
  When a Network is created, it reuses the parameter from the
  model. If a Network is built without given a name, the name "default" will
  be used.
  
  ***
  Do not call tf.global_variable_initializers() after building a model as it
  will reassign random weights to the model.
  The parameters inside a model will be initialized when calling build().
  ***
  
  Pickling is handled automatcailly. The target weights should be assigned to
  self._default_parameters before pickling, so that the newly created model
  can check if target weights exist or not. When unpickled, the unserialized
  model will load the weights from self._default_parameters.
  
  The design is illustrated as the following:
  
       input_1                      input_2
          |                            |
  ============== Model (TfModel)===================
  |       |                            |          |
  |       |            Parameters      |          |
  |    =============  /           \  ============ |
  |    |  default  | /             \ | Network2 | |
  |    | (Network) |/               \|(Network) | |
  |    =============                 ============ |
  |       |                            |          |
  =================================================
          |                            |
          |                            |
  (outputs from 'default' networks)    |
                      outputs from ['Network2'] network
  
  
  Examples are also available in tests/garage/tf/models/test_model.
  
  Args:
    name (str): Name of the model. It will also become the variable scope
        of the model. Every model should have a unique name.
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docstr: Build this model given input placeholder(s).
  
  User should implement _build() inside their subclassed model,
  and construct the computation graphs in this function.
  
  Args:
      inputs: Tensor input(s), recommended to be position arguments, e.g.
          def _build(self, state_input, action_input, name=None).
          It would be usually same as the inputs in build().
      name (str): Inner model name, also the variable scope of the
          inner model, if exist. One example is
          garage.tf.models.Sequential.
  
  Return:
      list[tf.Tensor]: Tensor output(s) of the model.
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code: def _build(self, *inputs, name=None):
      """Build this model given input placeholder(s).
  
      User should implement _build() inside their subclassed model,
      and construct the computation graphs in this function.
  
      Args:
          inputs: Tensor input(s), recommended to be position arguments, e.g.
              def _build(self, state_input, action_input, name=None).
              It would be usually same as the inputs in build().
          name (str): Inner model name, also the variable scope of the
              inner model, if exist. One example is
              garage.tf.models.Sequential.
  
      Return:
          list[tf.Tensor]: Tensor output(s) of the model.
  
      """
  
imports: import abc
  from collections import namedtuple
  import warnings
  import tensorflow
  from garage.tf.models.module import Module
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj._build() without return types