docstr: generate the batch used in inference for each of the steps
code: def prepare_batch_at_step(
      self,
      tokens: torch.Tensor,
      maxlen: int,
      micro_batch_size: int,
      step: int,
      context_length: int,
      compute_attention_mask: bool = True,
  ) -> Tuple[List[torch.Tensor], List[int]]:
      """
      generate the batch used in inference for each of the steps
      """
      # types2use = None
      if step == 0:
          # Allocate memory for the entire context.
          set_inference_key_value_memory = True
          tokens2use = tokens[:, :context_length]
          positions2use = self.position_ids[:, :context_length]
          # not using type2use. uncomment it if it is used
          # if type_ids is not None:
          #     types2use = type_ids[:, :context_length]
      else:
          # Set this to false so the memory is not reallocated.
          set_inference_key_value_memory = False
          tokens2use = tokens[:, context_length - 1].view(
              micro_batch_size, -1
          )
          positions2use = self.position_ids[:, context_length - 1].view(
              micro_batch_size, -1
          )
          # not using type2use. uncomment it if it is used
          # if type_ids is not None:
          #     types2use = type_ids[:, context_length - 1].view(batch_size, -1)
  
      """Prepare batch for each of the inference steps"""
      attention_mask_repeat = None
      if compute_attention_mask:
          attention_mask_repeat = torch.concat(
              [self.attention_mask for _ in range(micro_batch_size)]
          )
  
      setkey_value_array = torch.tensor(
          [set_inference_key_value_memory] * micro_batch_size,
          device=torch.cuda.current_device(),
      )
      len_array = torch.tensor(
          [maxlen] * micro_batch_size,
          device=torch.cuda.current_device(),
      )
  
      batch = [
          tokens2use,
          attention_mask_repeat,
          positions2use,
          setkey_value_array,
          len_array,
      ]
      tensor_shape = [
          tokens2use.shape[1],
          micro_batch_size,
          self.model.cfg.hidden_size,
      ]
      return batch, tensor_shape
  
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 4 for calling the GPTModelTextGenerationStrategy obj's underlying member method code with example usage: obj.prepare_batch_at_step(tokens, maxlen, micro_batch_size, step, context_length, compute_attention_mask) and returns: batch,  tensor_shape