WeightedRandomSampler for custom image dataloader pytorch

Question

I am trying to solve class imbalance by using Weighted Random Sampler on a custom data loader. I can't seem to find the best way to implement this. The images are in a folder and labels are in a csv file. The dataloader code without the weighted random sampler is given below.

class CassavaDataset(Dataset):
    def __init__(self, df, data_root, transforms=None, output_label=True):
        super().__init__()
        self.df = df.reset_index(drop=True).copy() # data
        self.transforms = transforms
        self.data_root = data_root
        self.output_label = output_label
 
    def __len__(self):
        return self.df.shape[0] # or len(self.df)
    
    def __getitem__(self, index: int):
        
        # get labels
        if self.output_label:
            target = self.df.iloc[index]['label']
          
        path = "{}/{}".format(self.data_root, self.df.iloc[index]['image_id'])
        
        img  = get_img(path)

        if self.transforms:
            img = self.transforms(image=img)['image']
           
        # do label smoothing
        if self.output_label == True:
            return img, target
        else:
            return img

What will be the best way to get weights of each class and feed it to the sampler before augmentation? Thanks in advance!