code: def padded_stack(arrays):
      lens = [arr.shape[0] for arr in arrays]
      other_padding = [(0, 0) for _ in arrays[0].shape[1:]]
      max_len = max(lens)
      padded = [
          np.pad(a, ((0, max_len - l), *other_padding), "constant")
          for a, l in zip(arrays, lens)
      ]
      print([p.shape for p in arrays])
      print([p.shape for p in padded])
      return np.stack(padded, 0)
  
imports: import numpy
  from finetune.base import BaseModel
  from finetune.input_pipeline import BasePipeline
  from finetune.encoding.input_encoder import EncodedOutput
  from finetune.encoding.target_encoders import IDEncoder
  import tensorflow
  from finetune.nn.target_blocks import multi_choice_question
  from finetune.util import list_transpose
  from finetune.encoding.input_encoder import tokenize_context
  from finetune.model import PredictMode
label_desc: Use image node_id 1 for calling a global function with example usage: padded_stack(arrays) and returns: np