class_docstr: This is the configuration class to store the configuration of a [`~peft.AdaLora`].
  
  Args:
      target_r (`int`): The target average rank of incremental matrix.
      init_r (`int`): The initial rank for each incremental matrix.
      tinit (`int`): The steps of initial fine-tuning warmup.
      tfinal (`int`): The step of final fine-tuning.
      deltaT (`int`): The time internval between two budget allocations.
      beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.
      beta2 (`float`): The hyperparameter of EMA for undertainty quantification.
      orth_reg_weight (`float`): The coefficient of orthogonal regularization.
      total_step (`int`): The total training steps that should be specified before training.
      rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator.
code: def __post_init__(self):
      self.peft_type = PeftType.ADALORA
  
imports: from dataclasses import dataclass, field
  from typing import Optional
  from peft.tuners.lora import LoraConfig
  from peft.utils import PeftType
label_desc: Use image node_id 1 for calling the AdaLoraConfig obj's underlying member method code with example usage: obj.__post_init__() without return types