I have the following python function:

```python
def resize_images(images, res_x, res_y):
    resized_images = np.zeros((images.shape[0], res_x, res_y))
    for n in range(len(images)):
        resized_images[n, :, :] = resize(images[n, :, :], resized_images.shape[1:], anti_aliasing=False)
    return resized_images
```

What does the function do?
Hmm, let me see. I think the function is used for Image Resizing. It takes in an array of `images`, two integers related to resolution one is the x-axis: `res_x` and a second one on the y-axis: `res_y` as inputs. The function then loops through each image in the array and uses the resizing function (resize) from the Python library Scikit-image to resize the images to the specified resolution. Finally, the function returns an array of resized images. Here is an in-depth look:

```python
#It takes in a numpy array of images and two integers `res_x` and `res_y` as inputs and then returns a numpy array of resized images.
def resize_images(images, res_x, res_y):

# It creates a numpy array resized_images with dimensions (images.shape[0], res_x, res_y) filled with zeros, where images.shape[0] represents the number of images in the input array.
  resized_images = np.zeros((images.shape[0], res_x, res_y))

#It iterates over the length of the input images and for each image, it uses the resize function from the scikit-image library to resize the image to the specified `res_x` and `res_y` resolution, with the anti_aliasing boolean argument set to False. meaning that the resizing process will not apply anti-aliasing to the images.
  for n in range(len(images)): 
    resized_images[n, :, :] = resize(images[n, :, :], resized_images.shape[1:], anti_aliasing=False)

# Finally, the resized_images array is returned.
return resized_images
```