class_docstr: 可以将形如 ``[[1], [1, 2]]`` 这类的数据转为 ``jittor.Var([[1], [1, 2]])``
  
  :param pad_val: 该值无意义
  :param ele_dtype: 用于检测当前 field 的元素类型是否可以转换为 :class:`jittor.Var` 类型；
  :param dtype: 输出的数据的 dtype 是什么。如 :class:`jittor.long`, :class:`jittor.float32`, :class:`int`, :class:`float` 等；
docstr: :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
docstr_tok: [":","param","batch_field",":","\u8f93\u5165\u7684\u67d0\u4e2a","field","\u7684","batch","\u6570\u636e\u3002",":","param","pad_val",":","\u9700\u8981\u586b\u5145\u7684\u503c",":","param","dtype",":","\u6570\u636e\u7684\u7c7b\u578b"]
code: def pad(batch_field, pad_val=0, dtype=None):
      """
      :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
      """
      tensor = get_padded_jittor_tensor(
          batch_field, dtype=dtype, pad_val=pad_val
      )
      return tensor
  
imports: from inspect import isclass
  import numpy
  from fastNLP.envs.imports import _NEED_IMPORT_JITTOR
  from .padder import Padder
  from .utils import is_number_or_numpy_number, is_number, is_numpy_number_dtype, get_shape, is_numpy_generic_class
  from .exceptions import *
label_desc: Use image node_id 2 for calling the JittorSequencePadder obj's underlying member method code with example usage: obj.pad(batch_field, pad_val, dtype) and returns: tensor