code: def create_block(
      config,
      layer_idx=None,
      process_group=None,
      device=None,
      dtype=None,
  ):
      factory_kwargs = {"device": device, "dtype": dtype}
      sequence_parallel = getattr(config, "sequence_parallel", True)
      mixer_cls = create_mixer_cls(
          config,
          layer_idx,
          process_group=process_group,
          **factory_kwargs,
      )
      mlp_cls = create_mlp_cls(
          config,
          layer_idx,
          process_group=process_group,
          **factory_kwargs,
      )
      use_rms_norm = getattr(config, "rms_norm", False)
      norm_cls = partial(
          nn.LayerNorm if not use_rms_norm else RMSNorm,
          eps=config.layer_norm_epsilon,
          **factory_kwargs,
      )
      # TD [2022-07-30]: Force residual in fp32, seems to make fp16 training more stable
      residual_in_fp32 = getattr(config, "residual_in_fp32", False)
      resid_dropout1 = (
          config.resid_pdrop
          if layer_idx is None or layer_idx > 0
          else config.embd_pdrop
      )
      prenorm = getattr(config, "prenorm", True)
      parallel_block = getattr(config, "parallel_block", False)
      if not parallel_block:
          block = Block(
              config.hidden_size,
              mixer_cls,
              mlp_cls,
              norm_cls=norm_cls,
              prenorm=prenorm,
              resid_dropout1=resid_dropout1,
              resid_dropout2=config.resid_pdrop,
              fused_dropout_add_ln=getattr(
                  config, "fused_dropout_add_ln", False
              ),
              residual_in_fp32=residual_in_fp32,
              sequence_parallel=sequence_parallel
              and process_group is not None,
              mark_shared_params=process_group is not None,
          )
      else:
          assert prenorm
          block = ParallelBlock(
              config.hidden_size,
              mixer_cls,
              mlp_cls,
              norm_cls=norm_cls,
              resid_dropout1=resid_dropout1,
              resid_dropout2=config.resid_pdrop,
              tied_norm=getattr(
                  config, "parallel_block_tied_norm", False
              ),
              fused_dropout_add_ln=getattr(
                  config, "fused_dropout_add_ln", False
              ),
              residual_in_fp32=residual_in_fp32,
              sequence_parallel=sequence_parallel
              and process_group is not None,
              mark_shared_params=process_group is not None,
          )
      block.layer_idx = layer_idx
      return block
  
imports: import logging
  import math
  import re
  from collections import OrderedDict, namedtuple
  from collections.abc import Sequence
  from functools import partial
  from typing import Dict, List
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange
  from transformers import GPT2Config
  from flash_attn.models.bigcode import remap_state_dict_hf_bigcode
  from flash_attn.models.falcon import remap_state_dict_hf_falcon
  from flash_attn.models.gpt_neox import remap_state_dict_hf_gpt_neox
  from flash_attn.models.gptj import remap_state_dict_hf_gptj
  from flash_attn.models.llama import remap_state_dict_hf_llama
  from flash_attn.models.opt import remap_state_dict_hf_opt
  from flash_attn.modules.block import Block, ParallelBlock
  from flash_attn.modules.embedding import GPT2Embeddings, ParallelGPT2Embeddings
  from flash_attn.modules.mha import MHA, ParallelMHA
  from flash_attn.modules.mlp import FusedMLP, GatedMlp, Mlp, ParallelFusedMLP, ParallelGatedMlp, ParallelMLP
  from flash_attn.ops.activations import sqrelu_fwd
  from flash_attn.utils.distributed import all_gather, all_gather_raw, get_dim_for_local_rank, sync_shared_params
  from flash_attn.utils.generation import GenerationMixin
  from flash_attn.utils.pretrained import state_dict_from_pretrained
label_desc: Use image node_id 3 for calling a global function with example usage: create_block(config, layer_idx, process_group, device, dtype) and returns: block