code: def generate(
      self,
      inputs: Union[np.array, List[List[int]]],
      max_new_tokens: int = 32,
      do_sample: bool = False,
      temperature: float = 1.0,
      stop: Optional[int] = None,
      debug_mode: Optional[str] = None,
      cut_gen_len: Optional[int] = None,
      verbose: int = 0,
  ):
      task = Task(
          inputs=inputs,
          prompt_len=len(inputs[0]),
          gen_len=max_new_tokens,
          cut_gen_len=cut_gen_len,
          do_sample=do_sample,
          temperature=temperature,
          stop=stop,
      )
      assert stop is None, "Not implemented."
      num_pipeline_stages = self.num_pipeline_stages
      num_layers = self.num_layers
      num_gpu_batches = self.num_gpu_batches
      gpu_batch_size = self.policy.gpu_batch_size
      overlap = self.policy.overlap
      num_prompts = len(task.inputs)
      num_inner_iterations = self.num_inner_iterations
  
      assert num_prompts % (gpu_batch_size * num_gpu_batches) == 0
      num_pipeline_batches = num_prompts // (
          gpu_batch_size * num_gpu_batches
      )
      self.num_pipeline_batches = num_pipeline_batches
      assert num_pipeline_batches % num_inner_iterations == 0
      prompt_len, gen_len = task.prompt_len, task.gen_len
      self.execute_gen_len = (
          task.cut_gen_len if task.cut_gen_len else task.gen_len
      )
  
      # Output token ids
      self.output_ids = np.ones(
          (num_prompts, prompt_len + gen_len), dtype=np.int64
      )
      self.output_ids[:, :prompt_len] = np.asarray(task.inputs)
  
      # Intermediate tensors
      # The following buffers store values used
      # for the i-th token, j-th layer, k-th gpu batch, t-th stage.
      # cache[t][j][k]
      self.cache_home = array_3d(
          num_inner_iterations, num_layers, num_gpu_batches, ValueHolder
      )
      self.cache_read_buf = array_3d(
          num_inner_iterations, num_layers, num_gpu_batches, ValueHolder
      )
      self.cache_write_buf = array_3d(
          num_inner_iterations, num_layers, num_gpu_batches, ValueHolder
      )
      # weight[j]
      self.weight_read_buf = array_1d(num_layers, ValueHolder)
      # hidden[t][i][j][k]
      self.hidden = array_4d(
          num_inner_iterations,
          gen_len,
          num_layers,
          num_gpu_batches,
          ValueHolder,
      )
      # attention_mask[t][k]
      self.attention_mask = array_2d(
          num_inner_iterations, num_gpu_batches, ValueHolder
      )
  
      # Init cache
      self.set_task(task)
      for t in range(num_inner_iterations):
          for j in range(num_layers):
              for k in range(num_gpu_batches):
                  self.init_cache(t, j, k)
      if self.policy.cpu_cache_compute:
          self.env.cpu.init_attention_compute_workspace(
              self.config, self.task, self.policy
          )
  
      dist.barrier()
  
      # Generate
      if not overlap:
          # No overlap, easy to understand, suitable for debugging
          self.generation_loop_normal()
      else:
          # Overlap I/O and compute
          if self.policy.num_gpu_batches == 1:
              self.generation_loop_overlap_one_batch()
          else:
              self.generation_loop_overlap_multi_batch()
  
      # Delete cache
      for t in range(num_inner_iterations):
          for j in range(num_layers):
              for k in range(num_gpu_batches):
                  self.delete_cache(t, j, k)
      if self.policy.cpu_cache_compute:
          self.env.cpu.del_attention_compute_workspace()
  
      return self.output_ids
  
imports: import argparse
  from itertools import count
  import os
  import pickle
  import traceback
  from typing import Union, List, Optional
  import numpy
  import torch
  import torch.distributed
  from transformers import AutoTokenizer
  from flexgen.compression import CompressionConfig
  from flexgen.dist_utils import initialize_distributed
  from flexgen.flex_opt import Policy, InputEmbed, OutputEmbed, SelfAttention, MLP, TransformerLayer, OptLM, get_filename, add_parser_arguments, get_test_inputs, DUMMY_WEIGHT
  from flexgen.opt_config import get_opt_config
  from flexgen.pytorch_backend import TorchDevice, TorchDisk, TorchLink, TorchMixedDevice, TorchTensor
  from flexgen.timer import timers
  from flexgen.utils import Task, ExecutionEnv, GB, T, ValueHolder, array_1d, array_2d, array_3d, array_4d, str2bool, project_decode_latency
label_desc: Use image node_id 13 for calling the DistOptLM obj's underlying member method code with example usage: obj.generate(inputs, max_new_tokens, do_sample, temperature, stop, debug_mode, cut_gen_len, verbose) and returns: self