code: def __init__(self, params, lora_params, bid):
      self.bid = bid
      self.att_norm_a = Tensor(
          "f", [lora_params.n_rank_attention_norm, params.n_embd]
      )
      self.att_norm_b = Tensor(
          "f", [lora_params.n_rank_attention_norm, 1]
      )
      self.wq_a = Tensor("f", [lora_params.n_rank_wq, params.n_embd])
      self.wq_b = Tensor("f", [lora_params.n_rank_wq, params.n_embd])
      self.wk_a = Tensor("f", [lora_params.n_rank_wk, params.n_embd])
      self.wk_b = Tensor("f", [lora_params.n_rank_wk, params.n_embd])
      self.wv_a = Tensor("f", [lora_params.n_rank_wv, params.n_embd])
      self.wv_b = Tensor("f", [lora_params.n_rank_wv, params.n_embd])
      self.wo_a = Tensor("f", [lora_params.n_rank_wo, params.n_embd])
      self.wo_b = Tensor("f", [lora_params.n_rank_wo, params.n_embd])
      self.ffn_norm_a = Tensor(
          "f", [lora_params.n_rank_ffn_norm, params.n_embd]
      )
      self.ffn_norm_b = Tensor("f", [lora_params.n_rank_ffn_norm, 1])
      self.w1_a = Tensor("f", [lora_params.n_rank_w1, params.n_embd])
      self.w1_b = Tensor(
          "f", [lora_params.n_rank_w1, params.get_n_ff()]
      )
      self.w2_a = Tensor(
          "f", [lora_params.n_rank_w2, params.get_n_ff()]
      )
      self.w2_b = Tensor("f", [lora_params.n_rank_w2, params.n_embd])
      self.w3_a = Tensor("f", [lora_params.n_rank_w3, params.n_embd])
      self.w3_b = Tensor(
          "f", [lora_params.n_rank_w3, params.get_n_ff()]
      )
  
imports: import argparse
  import gguf
  import struct
  import numpy
  from pathlib import Path
label_desc: Use image node_id 1 to create a new Layer object  with example: obj = Layer(params, lora_params, bid)