code: def load_col(config, prefix: str, weights, bias: bool):
      if config.transpose:
          weight = weights.get_sharded(f"{prefix}.weight", dim=1).T
      else:
          weight = weights.get_multi_weights_col(
              [prefix], quantize=config.quantize, dim=0
          )
  
      if bias:
          bias = weights.get_sharded(f"{prefix}.bias", dim=0)
      else:
          bias = None
      return TensorParallelColumnLinear(
          get_linear(weight, bias, config.quantize)
      )
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from typing import Optional, List, Tuple
  from text_generation_server.utils import paged_attention, flash_attn
  from text_generation_server.utils.layers import TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelHead, TensorParallelEmbedding, FastLayerNorm, get_linear
label_desc: Use image node_id 4 for calling a global function with example usage: load_col(config, prefix, weights, bias) and returns: TensorParallelColumnLinear