docstr: Process raw inputs into model input tensors, it supports two input
  formats:
      1) multiple rows of texts (single sentence or a pair)
      2) multiple rows of pre-processed tokens (single sentence or a pair)
  
  This function should handle the logic of calling numberize() and also
  padding the numberized result.
code: def tensorize(
      self,
      texts: Optional[List[List[str]]] = None,
      tokens: Optional[List[List[List[str]]]] = None,
  ):
      """
      Process raw inputs into model input tensors, it supports two input
      formats:
          1) multiple rows of texts (single sentence or a pair)
          2) multiple rows of pre-processed tokens (single sentence or a pair)
  
      This function should handle the logic of calling numberize() and also
      padding the numberized result.
      """
      raise NotImplementedError
  
imports: from typing import List, Optional, Tuple
  import torch
  from pytext.torchscript.utils import pad_2d_float, pad_3d_float, validate_padding_control
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytorch.text.fb.utils.transform_utils import pad_2d
label_desc: Use image node_id 6 for calling the ScriptTensorizer obj's underlying member method code with example usage: obj.tensorize(texts, tokens) without return types