docstr: <a id="post_load_prepare"></a>
  
  ### Layer transformations after loading the checkpoint
  
  This function implements layer transformations after loading the checkpoint.
  
  Currently, it only applies the int8 quantization.
  
  :param layer: is the layer to prepare
  :param is_llm_int8: specifies whether to use int8 quantization
  :param device: is the device of the model
  :param llm_int8_threshold: is the threshold $lpha$ used to separate outlier features
  :return: the prepared layer
code: def post_load_prepare(
      self,
      layer: NeoXModule,
      *,
      is_llm_int8: bool = None,
      device: torch.device = None,
      llm_int8_threshold: float = None,
  ):
      """
      <a id="post_load_prepare"></a>
  
      ### Layer transformations after loading the checkpoint
  
      This function implements layer transformations after loading the checkpoint.
  
      Currently, it only applies the int8 quantization.
  
      :param layer: is the layer to prepare
      :param is_llm_int8: specifies whether to use int8 quantization
      :param device: is the device of the model
      :param llm_int8_threshold: is the threshold $\alpha$ used to separate outlier features
      :return: the prepared layer
      """
  
      # Get default values if not specified
      if is_llm_int8 is None:
          is_llm_int8 = self.is_llm_int8
      if device is None:
          device = self.device
      if llm_int8_threshold is None:
          llm_int8_threshold = self.llm_int8_threshold
  
      # Skip if not using int8 quantization
      if not is_llm_int8:
          return layer
  
      # Only convert the linear layers in the transformer layers
      if not isinstance(layer, TransformerLayer):
          return layer
  
      # Use `make_llm_int8_linear` defined in [utilities](./utils/llm_int8.html).
      from labml_nn.neox.utils.llm_int8 import make_llm_int8_linear
  
      # Convert the linear layers
      with monit.section("Convert to int8"):
          layer.attention.output = make_llm_int8_linear(
              layer.attention.output,
              device=device,
              threshold=llm_int8_threshold,
          )
          layer.attention.qkv_lin = make_llm_int8_linear(
              layer.attention.qkv_lin,
              device=device,
              threshold=llm_int8_threshold,
          )
          layer.ffn.dense_h_h4 = make_llm_int8_linear(
              layer.ffn.dense_h_h4,
              device=device,
              threshold=llm_int8_threshold,
          )
          layer.ffn.dense_h4_h = make_llm_int8_linear(
              layer.ffn.dense_h4_h,
              device=device,
              threshold=llm_int8_threshold,
          )
      #
      return layer
  
imports: import copy
  import math
  from typing import Dict, Optional, Set, Callable, Any, Generator, Tuple
  import torch
  from torch import nn
  from torch.cuda.amp import autocast
  from labml import monit, logger
  from labml.logger import Text
  from labml_nn.neox import checkpoint
  from labml_nn.neox.utils.cache import get_cache
label_desc: Use image node_id 3 for calling the LayerGenerator obj's underlying member method code with example usage: obj.post_load_prepare(layer) and returns: layer, layer, layer