code: def linear4bit_to_linear(linear4bit_layer):
      """Converts a Linear4Bit layer to a standard Linear layer by dequantizing the weight values and copying the
      dequantized weights to a new Linear layer.
  
      Args:
          linear4bit_layer (Linear4bit): The input Linear4Bit layer.
  
      Returns:
          nn.Linear: A new Linear layer with dequantized weights and biases.
      """
      # Create a new Linear layer with the same shape
      new_linear_layer = nn.Linear(
          linear4bit_layer.in_features,
          linear4bit_layer.out_features,
          bias=linear4bit_layer.bias is not None,
          dtype=torch.float16,
      )
  
      # Dequantize the weight and bias from the Linear4bit layer and perform an in-place tensor replacement
      # to update the weights and bias in the new Linear layer. This is done to avoid creating a new tensor
      # and copying the data, which is slow.
      new_linear_layer.weight.data.copy_(
          dequantize_4bit(
              linear4bit_layer.weight.data,
              linear4bit_layer.weight.quant_state,
          )
      )
      if linear4bit_layer.bias is not None:
          new_linear_layer.bias.data.copy_(linear4bit_layer.bias.data)
  
      return new_linear_layer
  
imports: import torch
  from bitsandbytes.functional import dequantize_4bit
  from bitsandbytes.nn.modules import Linear4bit
  from torch import nn
  from ludwig.api_annotations import DeveloperAPI
label_desc: Use image node_id 1 for calling a global function with example usage: linear4bit_to_linear(linear4bit_layer) and returns: new_linear_layer