n-th quantile for bivariate variable I generate a 2000 bivariate random samples which are negative correlated. I used np.quantile to generate 10 quantile from this random samples. The related point is marked in the following figure. I am not clear how to explain why this 10-quantile point locate in this  position. The code to perform the simulate is attached as well. Or my question is, it is quite natural to explain the quantile for the one-dimensional distribution. But for the high -dimensional distribution, how to explain its quantile.
import numpy as np
import matplotlib.pyplot as plt
num_samples = 2000
mu = np.array([5.0, 2.0])
r = np.array([
        [  3.40, -2.75],
        [ -2.75,  1.0]       
    ])
rng = np.random.default_rng()
y = rng.multivariate_normal(mu, r, size=num_samples)
quantile_set = np.quantile(y, 0.1, axis=0)
plt.plot(y[:,0], y[:,1], 'b.', alpha=0.25)
plt.plot(quantile_set[0],quantile_set[1],'ro',ms=4.5)
plt.grid(True)



The documentation says
axis{int, tuple of int, None}, optional
Axis or axes along which the quantiles are computed. The default is to compute the quantile(s) along a flattened version of the array.

So, axis=0 asks for the 0.1 quantile along the first variable.
There are several proposed definitions for an actual multivariate quantile.  You could take the elementwise quantile, or a quantile based on data depth using triangles or half-spaces or convex hulls, or a point that minimises the sum of absolute values of residuals, or probably a bunch of other things. This is a useful overview.
