class_docstr: An alignprocessor align video and text and output a dict of tensors (for a model).
class_docstr_tok: ["An","alignprocessor","align","video","and","text","and","output","a","dict","of","tensors","(","for","a","model",")","."]
docstr: __init__ needs to be light weight for more workers/threads.
docstr_tok: ["__init__","needs","to","be","light","weight","for","more","workers\/threads","."]
code: def __init__(self, config):
      """__init__ needs to be light weight for more workers/threads."""
      self.split = config.split
      self.max_video_len = config.max_video_len
      self.max_len = config.max_len
      from transformers import AutoTokenizer
  
      tokenizer = AutoTokenizer.from_pretrained(
          str(config.bert_name), use_fast=config.use_fast
      )
      self.cls_token_id = tokenizer.cls_token_id
      self.sep_token_id = tokenizer.sep_token_id
      self.pad_token_id = tokenizer.pad_token_id
      self.mask_token_id = tokenizer.mask_token_id
  
imports: import numpy
  import os
  import torch
label_desc: Use image node_id 1 to create a new Aligner object from inherited base classes: object with example: obj = Aligner(config)