class_docstr: Pad the input ndarrays along the specific padding axis and stack them to get the output.
  Input of the function will be N samples. Each sample should contain a single element that
  can be 1) numpy.ndarray, 2) mxnet.nd.NDArray, 3) list of numbers.
  You can set the `pad_val` to determine the padding value.
  
  The arrays will be padded to the largest dimensions(at most 5 dimensions to pad) and then
  stacked to form the final output.
  
  Parameters
  ----------
  val : float or int, default None
      The padding value.
  dtype : str or numpy.dtype, default None
      The value type of the output. If it is set to None, the input data type is used.
  round_to : int, default None
      If specified, the padded dimension will be rounded to be multiple of this argument.
  
  Examples
  --------
  >>> from mxnet.gluon.data import batchify
  >>> # Inputs are multiple lists
  >>> a = [1, 2, 3, 4]
  >>> b = [4, 5, 6]
  >>> c = [8, 2]
  >>> batchify.Pad()([a, b, c])
  [[ 1  2  3  4]
   [ 4  5  6  0]
   [ 8  2  0  0]]
  <NDArray 3x4 @cpu(0)>
  >>> # Also output the lengths
  >>> a = [1, 2, 3, 4]
  >>> b = [4, 5, 6]
  >>> c = [8, 2]
  >>> # Inputs are multiple ndarrays
  >>> import numpy as np
  >>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>> b = np.array([[5, 8], [1, 2]])
  >>> batchify.Pad(val=-1)([a, b])
  [[[ 1  2  3  4]
    [ 5  6  7  8]]
   [[ 5  8 -1 -1]
    [ 1  2 -1 -1]]]
  <NDArray 2x2x4 @cpu(0)>
  >>> # Inputs are multiple NDArrays
  >>> import mxnet as mx
  >>> a = nd.array([[1, 2, 3, 4], [5, 6, 7, 8]])
  >>> b = nd.array([[5, 8], [1, 2]])
  >>> batchify.Pad(val=-1)([a, b])
  [[[ 1.  2.  3.  4.]
    [ 5.  6.  7.  8.]]
   [[ 5.  8. -1. -1.]
    [ 1.  2. -1. -1.]]]
  <NDArray 2x2x4 @cpu(0)>
code: def __init__(
      self, val=None, dtype=None, round_to=None, use_shared_mem=False
  ):
      self._pad_val = 0 if val is None else val
      self._dtype = dtype
      self._warned = False
      self._round_to = round_to
      self._use_shared_mem = use_shared_mem
  
imports: import math
  import warnings
  import numpy
  from ...device import Device, cpu
  from ...None import ndarray
  from ...None import numpy
  from ...util import is_np_array
label_desc: Use image node_id 1 to create a new Pad object from inherited base classes: object with example: obj = Pad(val, dtype, round_to, use_shared_mem)