code: def get_layer_trainable_params(
      self, layer: NeoXModule, prefix: str
  ) -> Dict[str, nn.Parameter]:
      params = {}
  
      if isinstance(layer, TransformerLayer):
          # No need to train the mlp bias because we are adding it with attention output
          params[
              f"{prefix}.attention.output.bias"
          ] = layer.attention.output.bias
          params[
              f"{prefix}.attention.qkv_lin.bias"
          ] = layer.attention.qkv_lin.bias
          params[
              f"{prefix}.ffn.dense_h_h4.bias"
          ] = layer.ffn.dense_h_h4.bias
      else:
          pass
  
      return params
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/neox/utils/finetune.py
imports: from typing import List, Dict
  import torch
  from torch import nn
  from labml_nn.neox.model import TransformerLayer, NeoXModule
label_desc: Use image node_id 1 for calling the FineTuneBiases obj's underlying member method code with example usage: obj.get_layer_trainable_params(layer, prefix) and returns: params