docstr: 目前支持 ``[jittor.Var([3, 2], jittor.Var([1])]`` 类似的输入。若内部元素不为 :class:`jittor.Var` ，则必须含有 :meth:`tolist` 方法。
  
  :param pad_val: 需要 pad 的值；
  :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
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code: def __init__(self, pad_val=0, ele_dtype=None, dtype=None):
      """
      目前支持 ``[jittor.Var([3, 2], jittor.Var([1])]`` 类似的输入。若内部元素不为 :class:`jittor.Var` ，则必须含有 :meth:`tolist` 方法。
  
      :param pad_val: 需要 pad 的值；
      :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
      """
      dtype = _get_dtype(
          ele_dtype, dtype, class_name=self.__class__.__name__
      )
      super().__init__(pad_val=pad_val, dtype=dtype)
  
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 1 to create a new JittorTensorPadder object from inherited base classes: Padder with example: obj = JittorTensorPadder(pad_val, ele_dtype, dtype)