human
Solve the following python programming problem given the following code:
# Here we wrap the TensorFlow model so that it behaves more like a Numpy model 
# which the plot_decision_boundary function expects. Don't worry about 
# the details, it's just so we can visualise the decision boundary!

class TFModelWrapper(object):
    def __init__(self, model):
        self._model = model
    
    def get_logits(self, x): 
        return tf.get_default_session().run(self._model.get_logits(x_tf),
                                            feed_dict={x_tf : x,
                                                       y_tf : np.zeros(x.shape[0])})
    
wrapper = TFModelWrapper(tf_linear_model)
plot_decision_boundary(X, wrapper)
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

NOTE: Our final cost (0.786) matches the final cost reached in our Numpy implementation above. This is not by accident, but because we used exactly the same initial values for the parameters, the same optimizer with the same update rules on the same data. (The exact value may be different if you changed some of the HyperParameters)
Notice how much less code we had to write for the TensorFlow model compared to the Numpy one earlier! TensorFlow is designed for deep learning and provides a number of common functions. In fact, it's possible to define a linear model like this in even fewer lines in TensorFlow, but we wanted to make this example as clear as possible!
Visualizing the linear model's decision boundary