class_docstr: Interface-only abstract class for models.
  
  A Model contains the structure/configuration of a set of computation
  graphs, or can be understood as a set of networks. Using a model
  requires calling `build()` with given input placeholder, which can
  be either tf.compat.v1.placeholder, or the output from another model. This
  makes composition of complex models with simple models much easier.
  
  Examples:
      model = SimpleModel(output_dim=2)
      # To use a model, first create a placeholder.
      # In the case of TensorFlow, we create a tf.compat.v1.placeholder.
      input_ph = tf.compat.v1.placeholder(tf.float32, shape=(None, 2))
  
      # Building the model
      output = model.build(input_ph)
  
      # We can also pass the output of a model to another model.
      # Here we pass the output from the above SimpleModel object.
      model_2 = ComplexModel(output_dim=2)
      output_2 = model_2.build(output)
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docstr: Set parameters of the Model.
  
  Args:
      parameters (list[tf.Tensor]): Parameters.
docstr_tok: ["Set","parameters","of","the","Model",".","Args",":","parameters","(","list","[","tf.Tensor","]",")",":","Parameters","."]
code: def parameters(self, parameters):
      """Set parameters of the Model.
  
      Args:
          parameters (list[tf.Tensor]): Parameters.
  
      """
  
imports: import abc
  from collections import namedtuple
  import warnings
  import tensorflow
  from garage.tf.models.module import Module
label_desc: Use image node_id 4 for calling the BaseModel obj's underlying member method code with example usage: obj.parameters(parameters) without return types