ValueError: too many values to unpack (expected 2)

I am trying to follow this tutorial: land cover fastai
And while executing the workflow, I got this error:


This is the code where the error occurs:

def color_scale(arr):
    """correct the RGB bands to be a composed bands of value between 0 255 for visualization purpos
    
    Args:
        arr: RGB bands in numpy array 
    Return:
        str_arr: numpy array that values range from 0 to 255 for visualization
    """
    str_arr = (arr + 1) * 127.5
    return str_arr

def png_gen(patch, inference=False):
    """Save RGB, spectral info and labeled images for the coming deep learning
    
    Args:
        patch: Saved eopatches for deep learning LULC training and prediction 
    Return:
        None: images in the PNG.
    """
    
    patch_data = EOPatch.load(patch)
    patch_dir, patch_id = patch.split("/")
    if inference == True:
        inds_path = "inds_inference"

        if not op.isdir(inds_path):
            makedirs(inds_path)
        for i in range(len(bands)):
            #get NDVI, NDWI and NDBI from the bands
            inds = color_scale(bands[i][..., [-3, -2, -1]]).astype("uint8")
            Image.fromarray(inds).save(op.join(inds_path, patch+'_'+str(i)+'.png'))

    else:
        lulc = patch_data.mask_timeless['LULC']
        lulc_path = "lulc_all"
        rgb_path = "rgb_all"
        inds_path = "inds_all"
        if not op.isdir(lulc_path):
            makedirs(lulc_path)
        if not op.isdir(rgb_path):
            makedirs(rgb_path)
        if not op.isdir(inds_path):
            makedirs(inds_path)
        for i in range(len(bands)):
            # Switch R, G, B band index from bands
            rgb = color_scale(bands[i][..., [2, 1, 0]]).astype("uint8")
            inds = color_scale(bands[i][..., [-3, -2, -1]]).astype("uint8")
            Image.fromarray(rgb).save(op.join(rgb_path, "{}_{}.png".format(patch_id, str(i))))
            Image.fromarray(inds).save(op.join(inds_path, "{}_{}.png".format(patch_id, str(i))))
            Image.fromarray(lulc).save(op.join(lulc_path, "{}_{}.png".format(patch_id, str(i))))

And this is the execution code:

#download eopatch for the desired AOI and covert the numpy array into RGB, spectral info (NDVI, NDWI, NDBI) 
# and training lulc label into PNG and saved them under "rgb_all", "inds_all" and "lulc_all"
pbar = tqdm(total=len(patchIDs))
for idx, bbox in enumerate(tile_list[patchIDs]):
    # define additional parameters of the workflow
    extra_param = {
        add_data: {'bbox': bbox, 'time_interval': time_interval},
        save_s2: {'eopatch_folder': 'eopatch_{}'.format(idx)}
    }

    workflow.execute(extra_param)
    print("eopatch {} has been processed!".format(idx))
    # png_gen('eopatch_{}'.format(idx))
    png_gen(op.join(path_out_sampled, 'eopatch_{}'.format(idx)), inference=False)
    print("Saving RGB, LULC and Inds PNG to eopatch_{} for the coming ML pipeline".format(idx))
    # del 'eopatch_{}'.format(idx)
    shutil.rmtree(op.join(path_out_sampled, 'eopatch_{}'.format(idx)), ignore_errors=True)
    print("eopatch_{} deleted!".format(idx))
    pbar.update(1)

Instead of ‘/’ I tried ‘_’ in this line patch_dir, patch_id = patch.split("/") because the downloaded patches have this format “eopatch_idx”, but I got the same error.