code: def load(self, data, offset):
      offset = self.params.load(data, offset)
      offset = self.lora_params.load(data, offset)
  
      self.tok_embd_a = Tensor(
          "f",
          [self.lora_params.n_rank_tok_embeddings, self.params.n_embd],
      )
      self.tok_embd_b = Tensor(
          "f",
          [self.lora_params.n_rank_tok_embeddings, self.params.n_vocab],
      )
      self.norm_a = Tensor(
          "f", [self.lora_params.n_rank_norm, self.params.n_embd]
      )
      self.norm_b = Tensor("f", [self.lora_params.n_rank_norm, 1])
      self.output_a = Tensor(
          "f", [self.lora_params.n_rank_output, self.params.n_embd]
      )
      self.output_b = Tensor(
          "f", [self.lora_params.n_rank_output, self.params.n_vocab]
      )
  
      offset = self.tok_embd_a.load(data, offset)
      offset = self.tok_embd_b.load(data, offset)
      offset = self.norm_a.load(data, offset)
      offset = self.norm_b.load(data, offset)
      offset = self.output_a.load(data, offset)
      offset = self.output_b.load(data, offset)
  
      self.layers.clear()
      for bid in range(self.params.n_layer):
          layer = Layer(self.params, self.lora_params, bid)
          offset = layer.load(data, offset)
          self.layers.append(layer)
  
      return offset
  
imports: import argparse
  import gguf
  import struct
  import numpy
  from pathlib import Path
label_desc: Use image node_id 2 for calling the LoraModel obj's underlying member method code with example usage: obj.load(data, offset) and returns: offset