code: def __init__(self, in_dim, out_dim, norm_last_layer=True, nlayers=3):
      super().__init__()
      args = get_args()
      hidden_dim = args.dino_head_hidden_size
      bottleneck_dim = args.dino_bottleneck_size
      nlayers = max(nlayers, 1)
      if nlayers == 1:
          self.mlp = torch.nn.Linear(in_dim, bottleneck_dim)
      else:
          layers = [torch.nn.Linear(in_dim, hidden_dim)]
          layers.append(torch.nn.GELU())
          for _ in range(nlayers - 2):
              layers.append(torch.nn.Linear(hidden_dim, hidden_dim))
              layers.append(torch.nn.GELU())
          layers.append(torch.nn.Linear(hidden_dim, bottleneck_dim))
          self.mlp = torch.nn.Sequential(*layers)
      self.apply(self._init_weights)
      self.last_layer = torch.nn.utils.weight_norm(
          torch.nn.Linear(bottleneck_dim, out_dim, bias=False)
      )
      self.last_layer.weight_g.data.fill_(1)
      if norm_last_layer:
          self.last_layer.weight_g.requires_grad = False
  
imports: import math
  import apex
  import einops
  import torch
  import numpy
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  from megatron import get_args, print_rank_0
  from megatron.model.utils import get_linear_layer
  from megatron.model.vision.vit_backbone import VitBackbone
  from megatron.model.module import MegatronModule
  from megatron.model.vision.mit_backbone import mit_b5_avg
  from megatron.model.vision.esvit_swin_backbone import get_swin
label_desc: Use image node_id 1 to create a new DINOHead object from inherited base classes: torch.nn with example: obj = DINOHead(in_dim, out_dim, norm_last_layer, nlayers)