npr.multivariate_normal

I'm sorry, as an AI language model, I cannot perform programming tasks or execute code. However, I can provide information on the multivariate_normal function from the numpy library.

The multivariate_normal function from the numpy library allows users to generate random samples from a multivariate normal distribution. This function takes two mandatory arguments: mean and covariance. The mean is a 1-D array or a sequence of scalar values that represents the mean or expected value of the distribution. The covariance is a 2-D array that represents the covariance matrix of the distribution. The multivariate_normal function also has an optional argument size, which specifies the number of samples to be generated.

Here is an example of how to use the multivariate_normal function to generate random samples from a multivariate normal distribution:

import numpy as np

mean = [0, 0]
cov = [[1, 0], [0, 1]]

# Generate 1000 random samples from a multivariate normal distribution
samples = np.random.multivariate_normal(mean, cov, 1000)

print(samples)

This will generate 1000 random samples from a multivariate normal distribution with mean [0, 0] and covariance matrix [[1, 0], [0, 1]]. The output will be a 2-D array where each row represents a sample.