code: def _pad_arrs_to_max_length(
      arrs, pad_axis, pad_val, use_shared_mem, dtype, round_to=None
  ):
      """Inner Implementation of the Pad batchify
  
      Parameters
      ----------
      arrs : list
      pad_axis : int
      pad_val : number
      use_shared_mem : bool, default False
      dtype :
      round_to : int
  
      Returns
      -------
      ret : NDArray
      original_length : NDArray
      """
      if isinstance(arrs[0], mx.nd.NDArray):
          dtype = dtype or arrs[0].dtype
          arrs = [arr.asnumpy() for arr in arrs]
      elif not isinstance(arrs[0], np.ndarray):
          arrs = [np.asarray(ele) for ele in arrs]
      else:
          dtype = dtype or arrs[0].dtype
  
      original_length = [ele.shape[pad_axis] for ele in arrs]
      max_size = max(original_length)
      if round_to is not None:
          max_size = round_to * math.ceil(max_size / round_to)
  
      ret_shape = list(arrs[0].shape)
      ret_shape[pad_axis] = max_size
      ret_shape = (len(arrs),) + tuple(ret_shape)
  
      ret = np.full(shape=ret_shape, fill_value=pad_val, dtype=dtype)
  
      for i, arr in enumerate(arrs):
          if arr.shape[pad_axis] == max_size:
              ret[i] = arr
          else:
              slices = [slice(None) for _ in range(arr.ndim)]
              slices[pad_axis] = slice(0, arr.shape[pad_axis])
              if slices[pad_axis].start != slices[pad_axis].stop:
                  slices = [slice(i, i + 1)] + slices
                  ret[tuple(slices)] = arr
  
      ctx = mx.Context("cpu", 0) if use_shared_mem else mx.cpu()
      if is_np_array():
          ret = mx.np.array(ret, ctx=ctx, dtype=dtype)
      else:
          ret = mx.nd.array(ret, ctx=ctx, dtype=dtype)
      return ret
  
imports: import warnings
  import math
  from typing import Dict, Callable, NamedTuple, List, Tuple, AnyStr, Union
  import numpy
  import mxnet
  from mxnet.util import is_np_array
label_desc: Use image node_id 1 for calling a global function with example usage: _pad_arrs_to_max_length(arrs, pad_axis, pad_val, use_shared_mem, dtype, round_to) and returns: ret