code: def get_ts_conf_range(
      all_confs: np.ndarray,
      df_target_stddev: dict = {},
      positive_domain: bool = False,
      std_tol: int = 1,
      group: Optional[str] = "__default",
      fixed_conf: float = None,
  ):
      all_significances = []
      all_conf_ranges = []
      for timestep in range(all_confs.shape[1]):
          sigs, confs = get_numeric_conf_range(
              all_confs[:, timestep, :, :],
              df_target_stddev,
              positive_domain,
              std_tol,
              group,
              fixed_conf,
          )
          all_significances.append(sigs)
          all_conf_ranges.append(confs)
  
      return np.vstack(all_significances).T, np.stack(
          all_conf_ranges
      ).swapaxes(0, 1)
  
file_path: lightwood/lightwood/analysis/nc/util.py
imports: from typing import Union, Optional
  import torch
  import numpy
  import pandas
  from torch.nn.functional import softmax
  from type_infer.dtype import dtype
label_desc: Use image node_id 5 for calling a global function with example usage: get_ts_conf_range(all_confs, df_target_stddev, positive_domain, std_tol, group, fixed_conf) and returns: np,  np