In Pytorch, how to test simple image with my loaded model?

I made a alphabet classification CNN model using Pytorch, and then use that model to test it with a single image that I've never seen before. I extracted a bounding box in my handwriting image with opencv, but I don't know how to apply it to the model.

enter image description here

import cv2
import matplotlib.image as mpimg
im = cv2.imread('/content/drive/My Drive/my_handwritten.jpg')

gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)

contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[1]
rects=[]

for cnt in contours:
  x, y, w, h = cv2.boundingRect(cnt)
  if h < 20: continue 
  red = (0, 0, 255)
  cv2.rectangle(im, (x, y), (x+w, y+h), red, 2)
  rects.append((x,y,w,h))

cv2.imwrite('my_handwritten_bounding.png', im) 

img_result = []
img_for_class = im.copy()

margin_pixel = 60

for rect in rects:
    #[y:y+h, x:x+w]
    img_result.append(
        img_for_class[rect[1]-margin_pixel : rect[1]+rect[3]+margin_pixel, 
                      rect[0]-margin_pixel : rect[0]+rect[2]+margin_pixel])

    # Draw the rectangles
    cv2.rectangle(im, (rect[0], rect[1]), 
                  (rect[0] + rect[2], rect[1] + rect[3]), (0, 0, 255), 2) 

count = 0
nrows = 4
ncols = 7

plt.figure(figsize=(12,8))

for n in img_result:
    count += 1
    plt.subplot(nrows, ncols, count)
    plt.imshow(cv2.resize(n,(28,28)), cmap='Greys', interpolation='nearest')

plt.tight_layout()
plt.show()