Please help fill in the missing code below in order for it run correctly. Areas that are needed/missing are in highlighted in bold.

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

#1) Generate the synthetic data using the following Python code snippet.

# Generate synthetic data

N = 100

# Zeros form a Gaussian centered at (-1, -1)

x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)), cov=.1*np.eye(2), size=(N//2,))

y_zeros = np.zeros((N//2,))

# Ones form a Gaussian centered at (1, 1)

x_ones = np.random.multivariate_normal(mean=np.array((1, 1)), cov=.1*np.eye(2), size=(N//2,))

y_ones = np.ones((N//2,))

x_np = np.vstack([x_zeros, x_ones])

y_np = np.concatenate([y_zeros, y_ones])

# Plot x_zeros and x_ones on the same graph

plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0')

plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1')

plt.legend()

plt.show()

#3) Generate a TensorFlow graph.

with tf.name_scope("placeholders"):

x = tf.constant(x_np, dtype=tf.float32)

y = tf.constant(y_np, dtype=tf.float32)

with tf.name_scope("weights"):

W = tf.Variable(tf.random.normal((2, 1)))

b = tf.Variable(tf.random.normal((1,)))

with tf.name_scope("prediction"):

y_logit = tf.squeeze(tf.matmul(x, W) + b)

# the sigmoid gives the class probability of 1

y_one_prob = tf.sigmoid(y_logit)

# Rounding P(y=1) will give the correct prediction.

y_pred = tf.round(y_one_prob)

with tf.name_scope("loss"):

# Compute the cross-entropy term for each datapoint

entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y)

# Sum all contributions

l = tf.reduce_sum(entropy)

with tf.name_scope("optim"):

train_op = tf.compat.v1.train.AdamOptimizer(.01).minimize(l)

with tf.name_scope("summaries"):

tf.compat.v1.summary.scalar("loss", l)

merged = tf.compat.v1.summary.merge_all()

train_writer = tf.compat.v1.summary.FileWriter('logistic-train', tf.compat.v1.get_default_graph())

#4) Train the model, get the weights, and make predictions.

#5) Plot the predicted outputs on top of the data.