y_data = 9.0 * x_data + 2.0 * x_data + 8.0 * x_data + 1.0 + np.random.randn( *x_data.shape) * 0.4

This code is not complete and contains errors. It appears to be attempting to create a set of y values based on a set of x values. However, it is not clear what the intended operation is for each term in the equation.

Here is an example of how the code could be rewritten to create a set of y values that are a combination of the x values and some random noise:

import numpy as np

x_data = np.linspace(-10, 10, 100)
noise = np.random.randn(*x_data.shape) * 0.4
y_data = 9.0 * x_data + 2.0 * x_data - 8.0 * x_data + 1.0 + noise

In this version, the y values are created by multiplying the x values by some constants (9.0, 2.0, and -8.0), adding a constant term (1.0), and adding some random noise. The noise is generated using the `numpy.random.randn()` function, which creates an array of random numbers with a normal distribution. The `*x_data.shape` syntax is used to ensure that the noise array has the same shape as the x data.