code: def expand_like(
      arrays: List[np.ndarray], fill: float = -100
  ) -> np.ndarray:
      """
      Stacks a list of arrays along the first dimension; the arrays are allowed to differ in
      the second dimension but should match for dim > 2.
  
      The output will have dimension
      (sum([l.shape[0] for l in arrays]), max([l.shape[1] for l in in arrays]), ...)
      For arrays that have fewer entries in the second dimension than the max, we will
      pad with the fill value.
  
      Args:
          arrays: List of np.ndarray to stack along the first dimension
          fill: Value to fill in when padding to max size in the second dimension
  
      Returns:
          stacked array
      """
      full_shape = list(arrays[0].shape)
      if len(full_shape) == 1:
          return np.concatenate(arrays)
      full_shape[0] = sum(a.shape[0] for a in arrays)
      full_shape[1] = max(a.shape[1] for a in arrays)
      result = np.full(full_shape, fill)
      row_offset = 0
      for a in arrays:
          result[row_offset : row_offset + a.shape[0], : a.shape[1]] = a
          row_offset += a.shape[0]
      return result
  
file_path: determined/model_hub/model_hub/utils.py
imports: import logging
  import os
  import typing
  import urllib.parse
  from typing import Any, Dict, List, Union
  import filelock
  import numpy
  import requests
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: expand_like(arrays, fill) and returns: result, np