class_docstr: 将类似于 ``[[1], [1, 2]]`` 的内容 pad 为 ``np.array([[1, 0], [1, 2]])``， 可以 pad 多重嵌套的数据。
  可以通过以下的方式直接使用:
  
      >>> NumpySequencePadder.pad([[1], [1, 2]], pad_val=-100, dtype=float)
      [[   1. -100.]
       [   1.    2.]]
  
  :param pad_val: pad 的值是多少；
  :param ele_dtype: 用于检测当前 field 的元素类型是否可以转换为 :class:`np.array` 类型；
  :param dtype: 输出的数据的 dtype ；
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docstr: 将 ``batch_field`` 数据 转为 :class:`numpy.array` 并 pad 到相同长度。
  
  :param batch_field: 输入的某个 field 的 batch 数据。
  :param pad_val: 需要填充的值
  :param dtype: 数据的类型
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code: def pad(batch_field, pad_val=0, dtype=None):
      """
      将 ``batch_field`` 数据 转为 :class:`numpy.array` 并 pad 到相同长度。
  
      :param batch_field: 输入的某个 field 的 batch 数据。
      :param pad_val: 需要填充的值
      :param dtype: 数据的类型
      """
      return get_padded_numpy_array(
          batch_field, dtype=dtype, pad_val=pad_val
      )
  
file_path: fastNLP/fastNLP/core/collators/padders/numpy_padder.py
imports: from numbers import Number
  from abc import ABC
  from typing import Any, Union
  import numpy
  from .padder import Padder
  from .utils import get_padded_numpy_array, is_number_or_numpy_number
  from .exceptions import *
label_desc: Use image node_id 2 for calling the NumpySequencePadder obj's underlying member method code with example usage: obj.pad(batch_field, pad_val, dtype) and returns: get_padded_numpy_array