code: def load_row(config, prefix: str, weights, bias: bool):
      weight = weights.get_multi_weights_row(
          prefix, quantize=config.quantize
      )
  
      if bias and weights.process_group.rank() == 0:
          # Rank is only on the first rank process
          bias = weights.get_tensor(f"{prefix}.bias")
      else:
          bias = None
  
      linear = get_linear(weight, bias, config.quantize)
      if config.use_parallel_residual:
          return linear
      else:
          return TensorParallelRowLinear(
              linear, process_group=weights.process_group
          )
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.gpt_neox import GPTNeoXConfig
  from typing import Optional, List, Tuple
  from text_generation_server.utils import paged_attention, flash_attn
  from text_generation_server.utils.flash_attn import attention
  from text_generation_server.utils.layers import TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelHead, FastLayerNorm, PositionRotaryEmbedding, get_linear
label_desc: Use image node_id 1 for calling a global function with example usage: load_row(config, prefix, weights, bias) and returns: linear, TensorParallelRowLinear