code: def __init__(
      self,
      dim_in,
      dim_out,
      mlp_dim,
      num_layers,
      bn_on=False,
      bias=True,
      flatten=False,
      xavier_init=True,
      bn_sync_num=1,
      global_sync=False,
  ):
      super(MLPHead, self).__init__()
      self.flatten = flatten
      b = False if bn_on else bias
      # assert bn_on or bn_sync_num=1
      mlp_layers = [nn.Linear(dim_in, mlp_dim, bias=b)]
      mlp_layers[-1].xavier_init = xavier_init
      for i in range(1, num_layers):
          if bn_on:
              if global_sync or bn_sync_num > 1:
                  mlp_layers.append(
                      NaiveSyncBatchNorm1d(
                          num_sync_devices=bn_sync_num,
                          global_sync=global_sync,
                          num_features=mlp_dim,
                      )
                  )
              else:
                  mlp_layers.append(
                      nn.BatchNorm1d(num_features=mlp_dim)
                  )
          mlp_layers.append(nn.ReLU(inplace=True))
          if i == num_layers - 1:
              d = dim_out
              b = bias
          else:
              d = mlp_dim
          mlp_layers.append(nn.Linear(mlp_dim, d, bias=b))
          mlp_layers[-1].xavier_init = xavier_init
      self.projection = nn.Sequential(*mlp_layers)
  
imports: from functools import partial
  import torch
  import torch.nn
  from detectron2.layers import ROIAlign
  import slowfast.utils.logging
  from slowfast.models.attention import MultiScaleBlock
  from slowfast.models.batchnorm_helper import NaiveSyncBatchNorm1d
  from slowfast.models.nonlocal_helper import Nonlocal
label_desc: Use image node_id 1 to create a new MLPHead object from inherited base classes: nn with example: obj = MLPHead(dim_in, dim_out, mlp_dim, num_layers, bn_on, bias, flatten, xavier_init, bn_sync_num, global_sync)