class_docstr: 将类似于 ``[[1], [1, 2]]`` 的内容 pad 为 ``torch.Tensor([[1, 0], [1, 2]])`` 可以 pad 多重嵌套的数据。
  
  :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:`torch.Tensor` 类型；
  :param dtype: 输出的数据的 dtype 是什么。如 :class:`torch.long`, :class:`torch.float32`, :class:`int`, :class:`float` 等；
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code: def pad(batch_field, pad_val=0, dtype=None):
      tensor = get_padded_torch_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_TORCH
  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 TorchSequencePadder obj's underlying member method code with example usage: obj.pad(batch_field, pad_val, dtype) and returns: tensor