code: def quantize_transformer_layer(
      orig_layer_impl, model, megatron=False, preln=False
  ):
      """Quantize bert-style transformer layers with DeepSpeed's transformer layer
      Arguments:
          orig_layer_impl (torch.nn.Module): the original transformer layer implementation to look for,
              e.g., transformers.models.bert.modeling_bert.BertLayer or transformers.BertLayer
          model (torch.nn.Module): user's nn.module representing their model
  
          megatron (bool): megatron model-parallel implementation (this is supported for inference only)
          preln (bool): does the original layer implementation do pre or post layer norm?
  
          Note: For Bert kind of models, we inject based on the DeepSpeed-Example models, if not setting huggingface flag.
  
      Returns:
          Updated nn.module with quantized transformer layers
      """
  
      def quantize_weight(weight):
          return weight.to(torch.int8)
  
      def megatron_layer_quantize(layer):
          layer.attention.query_key_value.weight.data = quantize_weight(
              layer.attention.query_key_value.weight.data
          )
          layer.attention.dense.weight.data = quantize_weight(
              layer.attention.dense.weight.data
          )
          layer.mlp.dense_h_to_4h.weight.data = quantize_weight(
              layer.mlp.dense_h_to_4h.weight.data
          )
          layer.mlp.dense_4h_to_h.weight.data = quantize_weight(
              layer.mlp.dense_4h_to_h.weight.data
          )
  
      def bert_layer_quantize(layer):
          layer.attention.self.query.weight.data = quantize_weight(
              layer.attention.self.query.weight.data
          )
          layer.attention.self.key.weight.data = quantize_weight(
              layer.attention.self.key.weight.data
          )
          layer.attention.self.value.weight.data = quantize_weight(
              layer.attention.self.value.weight.data
          )
          layer.attention.output.dense.weight.data = quantize_weight(
              layer.attention.output.dense.weight.data
          )
          if preln:
              layer.intermediate.dense_act.weight.data = (
                  quantize_weight(
                      layer.intermediate.dense_act.weight.data
                  )
              )
          else:
              layer.intermediate.dense.weight.data = quantize_weight(
                  layer.intermediate.dense.weight.data
              )
          layer.output.dense.weight.data = quantize_weight(
              layer.output.dense.weight.data
          )
  
      def quantize_fn(child):
          if megatron:
              # Quantize megatron GPT2 / GPT3 trained model
              megatron_layer_quantize(child)
          else:
              # Quantize either DeepSpeed or HuggingFace trained model
              bert_layer_quantize(child)
  
          return child
  
      return quantize_module(
          model=model,
          orig_class=orig_layer_impl,
          quantize_fn=quantize_fn,
      )
  
imports: import torch
label_desc: Use image node_id 1 for calling a global function with example usage: quantize_transformer_layer(orig_layer_impl, model, megatron, preln) and returns: quantize_module, weight, child