code: def get_argmin_mat(
      timestamps_in_scales: List[torch.Tensor],
  ) -> List[torch.Tensor]:
      """
      Calculate the mapping between the base scale and other scales. A segment from a longer scale is
      repeatedly mapped to a segment from a shorter scale or the base scale.
  
      Args:
          timestamps_in_scales (list):
              List containing timestamp tensors for each scale.
              Each tensor has dimensions of (Number of base segments) x 2.
  
      Returns:
          session_scale_mapping_list (list):
              List containing argmin arrays indexed by scale index.
      """
      scale_list = list(range(len(timestamps_in_scales)))
      segment_anchor_list = []
      for scale_idx in scale_list:
          time_stamps_float = timestamps_in_scales[scale_idx]
          segment_anchor_list.append(
              torch.mean(time_stamps_float, dim=1)
          )
      base_scale_idx = max(scale_list)
      base_scale_anchor = segment_anchor_list[base_scale_idx]
      session_scale_mapping_list = []
      for scale_idx in scale_list:
          curr_scale_anchor = segment_anchor_list[scale_idx]
          curr_mat = torch.tile(
              curr_scale_anchor, (base_scale_anchor.shape[0], 1)
          )
          base_mat = torch.tile(
              base_scale_anchor, (curr_scale_anchor.shape[0], 1)
          ).t()
          argmin_mat = torch.argmin(
              torch.abs(curr_mat - base_mat), dim=1
          )
          session_scale_mapping_list.append(argmin_mat)
      return session_scale_mapping_list
  
imports: from typing import Dict, List, Tuple
  import torch
  from torch.linalg import eigh, eigvalsh
label_desc: Use image node_id 12 for calling a global function with example usage: get_argmin_mat(timestamps_in_scales) and returns: session_scale_mapping_list