class_docstr: This is the configuration class to store the configuration of a [`OFTModel`].
  
  Args:
      r (`int`): OFT rank.
      module_dropout (`int`): The dropout probability for disabling OFT modules during training.
      target_modules (`Optional[Union[List[str], str]]`):
          The names of the modules to apply the adapter to. If this is specified, only the modules with the specified
          names will be replaced. When passing a string, a regex match will be performed. When passing a list of
          strings, either an exact match will be performed or it is checked if the name of the module ends with any
          of the passed strings. If this is specified as 'all-linear', then all linear modules are chosen, excluding
          the output layer. If this is not specified, modules will be chosen according to the model architecture. If
          the architecture is not known, an error will be raised -- in this case, you should specify the target
          modules manually.
      init_weights (`bool`):
          Whether to perform initialization of OFT weights.
      layers_to_transform (`Union[List[int], int]`):
          The layer indices to transform. If a list of ints is passed, it will apply the adapter to the layer indices
          that are specified in this list. If a single integer is passed, it will apply the transformations on the
          layer at this index.
      layers_pattern (`str`):
          The layer pattern name, used only if `layers_to_transform` is different from `None`.
      rank_pattern (`dict`):
          The mapping from layer names or regexp expression to ranks which are different from the default rank
          specified by `r`.
      modules_to_save (`List[str]`):
          List of modules apart from adapter layers to be set as trainable and saved in the final checkpoint.
      coft (`bool`):
          Whether to use the constrainted variant of OFT or not, off by default.
      eps (`float`):
          The control strength of COFT. The freedom of rotation. Only has an effect if `coft` is set to True.
      block_share (`bool`):
          Whether to share the OFT parameters between blocks or not. This is `False` by default.
code: def __post_init__(self):
      self.peft_type = PeftType.OFT
      self.target_modules = (
          set(self.target_modules)
          if isinstance(self.target_modules, list)
          else self.target_modules
      )
  
imports: from dataclasses import dataclass, field
  from typing import List, Optional, Union
  from peft.tuners.lycoris_utils import LycorisConfig
  from peft.utils import PeftType
label_desc: Use image node_id 1 for calling the OFTConfig obj's underlying member method code with example usage: obj.__post_init__() without return types