code: def isotonic(input_data, quantile_list):
      quantile_list = np.array(quantile_list).reshape(-1)
      batch_size = input_data.shape[0]
      new_output_data = []
      for i in range(batch_size):
          new_output_data.append(
              IsotonicRegression().fit_transform(
                  quantile_list, input_data[i]
              )
          )
      return np.stack(new_output_data, 0)
  
file_path: autogluon/tabular/src/autogluon/tabular/models/fastainn/quantile_helpers.py
imports: import numpy
  import torch
  import torch.nn
  from sklearn.isotonic import IsotonicRegression
label_desc: Use image node_id 1 for calling a global function with example usage: isotonic(input_data, quantile_list) and returns: np