Warping images using disparity maps for stereo matching

I am trying to warp an image using a disparity map. The original image size is (375, 1242) and same is the size for disparity map. Both, the image and disparity map, are grayscale images. Below is the code I tried to execute

import numpy as np
import torch
import torch.nn.functional as F

# Load the image (img) to be warped using PIL library
# Load the disparity map image (disp) using PIL library

disp = np.array(disp, dtype=np.float32) / 256.

x = np.array(img)
img = torch.Tensor(x)
img.resize_(1,3,375,1242)
batch_size,_,height, width = img.size()

# Original coordinates of pixels
x_base = torch.linspace(0, 1, width).repeat(batch_size, height, 1).type_as(img)
y_base = torch.linspace(0, 1, height).repeat(batch_size, width, 1).transpose(1, 2).type_as(img)

disp = torch.Tensor(disp)
disp.resize_(1,375,1242)

flow_field = torch.stack((x_base + disp, y_base), dim=3)

# In grid_sample coordinates are assumed to be between -1 and 1
output = F.grid_sample(img, 2*flow_field - 1, mode='bilinear', padding_mode='zeros')

The image is resized to (1, 3, 375, 1242) where 1 indicates the batch_size (since only one image is to be passed) and 3 indicates the number of channels.
The code is taken and modified from this source - MonoDepth-PyTorch/loss.py at 0b7d60bd1dab0e8b6a7a1bab9c0eb68ebda51c5c · OniroAI/MonoDepth-PyTorch · GitHub

However, I dont quite understand the working of flow_field variable and grid_sample() function.
The output image, after converting the ‘output’ variable from a tensor to PIL supported dataframe, is completely black i.e all the values of the output tensor turn out to be 0.

Can someone help in rectifying the above code ?