class_docstr: Base class for TextGeneration Strategy
class_docstr_tok: ["Base","class","for","TextGeneration","Strategy"]
docstr: initialize the batch data before the inference steps.
     It will save the intermediate results as object attributes
     context_length (int): the context token length
     compute_attention_mask: bool: set to True to compute attention mask (not needed for FA)
  Args:
      context_tokens (torch.Tensor):  The padded context tokens including the space for tokens to be generated 
code: def init_batch(
      self,
      context_tokens: torch.Tensor,
      context_length: int,
      compute_attention_mask: bool,
  ):
      """initialize the batch data before the inference steps.
         It will save the intermediate results as object attributes
         context_length (int): the context token length
         compute_attention_mask: bool: set to True to compute attention mask (not needed for FA)
      Args:
          context_tokens (torch.Tensor):  The padded context tokens including the space for tokens to be generated
      """
      pass
  
imports: import abc
  import copy
  import os
  import re
  import warnings
  from typing import List, Set, Tuple
  import torch
  from transformers import CLIPImageProcessor
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
label_desc: Use image node_id 5 for calling the TextGenerationStrategy obj's underlying member method code with example usage: obj.init_batch(context_tokens, context_length, compute_attention_mask) without return types