I want to use tqdm in a loop such as:

def __process_each_iteration(self, imputer) -> tuple[int, float, float]:
    progress_bar= tqdm(
        range(self.base_imputed_df.shape[1]),
        desc="Processing...: ",
        bar_format=(
            "{l_bar}{bar}| Iteration {n_fmt}/{total_fmt} "
            "[{elapsed}<{remaining}, {rate_fmt}]"
        ),
    )

    for col_index in progress_bar:
        pass
    progress_bar.close()
    

def fit_transform(self):
  for idx, imputer in enumerate(range(10)):
      change, avg_train_metric, avg_val_metric = self.__process_each_iteration(imputer)
      pass...

when I run the above code, it gives me the following output:

Iteration 1/9
Processing...: 100%|██████████| Iteration 30/30 [00:09<00:00, 3.26it/s]
Processing...: 0%| | Iteration 0/30 [00:00<?, ?it/s]30 columns updated.
Average r2_score -> train: 0.9665220914801507, val: 0.7951696912960284
Iteration 2/9
Processing...: 100%|██████████| Iteration 30/30 [00:13<00:00, 2.30it/s]
Processing...: 0%| | Iteration 0/30 [00:00<?, ?it/s]19 columns updated.
Average r2_score -> train: 0.9849819806147938, val: 0.85501137134333

it prints two progress bars in each iteration
I used tqdm as follows:

with tqdm(...) as

but I had the same problem…