docstr: initialize the batch data before the inference steps.
code: def init_batch(
      self,
      context_tokens: torch.Tensor,
      context_length: int,
      compute_attention_mask: bool,
  ):
      """initialize the batch data before the inference steps."""
      # Move to GPU.
      tokenizer = self.model.tokenizer
      tokens = context_tokens.contiguous().cuda()
      # Get the attention mask and postition ids.
      (
          self.attention_mask,
          _,
          self.position_ids,
      ) = get_ltor_masks_and_position_ids(
          tokens,
          tokenizer.eos_id,
          self.model.cfg.get("reset_position_ids", False),
          self.model.cfg.get("reset_attention_mask", False),
          self.model.cfg.get("eod_mask_loss", False),
          compute_attention_mask=compute_attention_mask,
      )
  
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 2 for calling the PromptLearningModelTextGenerationStrategy obj's underlying member method code with example usage: obj.init_batch(context_tokens, context_length, compute_attention_mask) without return types