code: def __init__(self, dim, init_values=1e-5, inplace=False):
      super().__init__()
      self.inplace = inplace
      self.gamma = nn.Parameter(init_values * torch.ones(dim))
  
imports: import logging
  from functools import partial
  from typing import Callable, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch.jit import Final
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, PatchDropout, LayerNorm2d, ClassifierHead, NormMlpClassifierHead, Format, resample_abs_pos_embed_nhwc, RotaryEmbeddingCat, apply_rot_embed_cat, to_2tuple, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
  from ._features_fx import register_notrace_function
label_desc: Use image node_id 1 to create a new LayerScale object from inherited base classes: nn with example: obj = LayerScale(dim, init_values, inplace)