I ended up combining into one csv with the location of my images as a column in my tabular data.  I have multiple images per record which means I take the mean prediction of one observation/mulitple images in the end. below is the code that worked on to pull the data.
class image_Dataset(Dataset):
‘’’
image class data set
'''
def __init__(self, data, transform = None):
    '''
    Args:
    ------------------------------------------------------------
        data = dataframe
        image = column in dataframe with absolute path to the image
        label = column in dataframe that is the target classification variable
        numerical_columns =  numerical columns from data
        categorical_columns = categorical columns from data
        policy = ID variable
        
    '''
    self.image_frame = data
    self.transform = transform
    
def __len__(self):
    return len(self.image_frame)

def __getitem__(self, idx):
    if torch.is_tensor(idx):
        idx = idx.tolist()
     
    label = self.image_frame.loc[idx, 'target']
    pic = Path(self.image_frame.loc[idx,'location'])
    img = Image.open(pic)
    policy = self.image_frame.loc[idx, 'policy']
    #sample = {'image': img, 'policy': policy, 'label':label}
    numerical_data = self.image_frame.loc[idx, numerical_columns]
    
    numerical_data = torch.tensor(numerical_data, dtype = torch.float)

    if self.transform:
        image = self.transform(img)
        
    for category in categorical_columns:
        self.image_frame[category] = self.image_frame[category].astype('category')
        
        self.image_frame[category] = self.image_frame[category].astype('category').cat.codes.values
    
        
    categorical_data = self.image_frame.loc[idx, categorical_columns]
    categorical_data = torch.tensor(categorical_data, dtype = torch.int64)
        
    return image, label, policy, categorical_data , numerical_data