force from * import * statements in all code snippets

docs/tex/tutorials/decoder.tex

@@ -49,7 +49,7 @@ \subsection{Probabilistic decoder}
 from keras.layers import Dense
 
 z = Normal(mu=tf.zeros([N, d]), sigma=tf.ones([N, d]))
-hidden = Dense(256, activation='relu')(z.value())
+hidden = Dense(256, activation='relu')(z)
 x = Bernoulli(logits=Dense(28 * 28)(hidden))
 \end{lstlisting}
 It starts with a $d$-dimensional standard normal prior, one for each

docs/tex/tutorials/gan.tex

@@ -37,6 +37,8 @@ \subsubsection{Data}
 TensorFlow placeholder with a fixed batch size of $M$ images.
 
 \begin{lstlisting}[language=Python]
+from tensorflow.examples.tutorials.mnist import input_data
+
 mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
 x_ph = tf.placeholder(tf.float32, [M, 784])
 \end{lstlisting}
@@ -65,6 +67,9 @@ \subsubsection{Model}
 $[0,1]$.
 
 \begin{lstlisting}[language=Python]
+from edward.models import Uniform
+from tensorflow.contrib import slim
+
 def generative_network(eps):
   h1 = slim.fully_connected(eps, 128, activation_fn=tf.nn.relu)
   x = slim.fully_connected(h1, 784, activation_fn=tf.sigmoid)

docs/tex/tutorials/latent-space-models.tex

@@ -69,6 +69,8 @@ \subsubsection{Model}
 
 In Edward, the model is written as follows:
 \begin{lstlisting}[language=Python]
+from edward.models import Normal, Poisson
+
 N = x_train.shape[0]  # number of data points
 K = 3  # latent dimensionality
 

docs/tex/tutorials/mixture-density-network.tex

@@ -20,6 +20,8 @@ \subsubsection{Data}
 for every input $x_n$ there are multiple outputs $y_n$.
 
 \begin{lstlisting}[language=Python]
+from sklearn.model_selection import train_test_split
+
 def build_toy_dataset(N):
   y_data = np.random.uniform(-10.5, 10.5, N).astype(np.float32)
   r_data = np.random.normal(size=N).astype(np.float32)  # random noise
@@ -64,6 +66,9 @@ \subsubsection{Model}
 units for each hidden layer.
 
 \begin{lstlisting}[language=Python]
+from edward.models import Categorical, Mixture, Normal
+from tensorflow.contrib import slim
+
 def neural_network(X):
   """mu, sigma, logits = NN(x; theta)"""
   # 2 hidden layers with 15 hidden units

docs/tex/tutorials/probabilistic-pca.tex

@@ -79,6 +79,8 @@ \subsubsection{Model}
 our variables of interest.
 
 \begin{lstlisting}[language=Python]
+from edward.models import Normal
+
 w = Normal(mu=tf.zeros([D, K]), sigma=2.0 * tf.ones([D, K]))
 z = Normal(mu=tf.zeros([N, K]), sigma=tf.ones([N, K]))
 x = Normal(mu=tf.matmul(w, z, transpose_b=True), sigma=tf.ones([D, N]))