code: def __init__(
      self,
      in_channels,
      num_classes,
      ins_dim=256,
      ins_conv=4,
      mask_dim=256,
      mask_conv=4,
      kernel_dim=128,
      scale_factor=2.0,
      output_iam=False,
      num_masks=100,
      act_cfg=dict(type="ReLU", inplace=True),
  ):
      super().__init__()
      # add 2 for coordinates
      in_channels = in_channels  # ENCODER.NUM_CHANNELS + 2
  
      self.scale_factor = scale_factor
      self.output_iam = output_iam
  
      self.inst_branch = InstanceBranch(
          in_channels,
          dim=ins_dim,
          num_convs=ins_conv,
          num_masks=num_masks,
          num_classes=num_classes,
          kernel_dim=kernel_dim,
          act_cfg=act_cfg,
      )
      self.mask_branch = MaskBranch(
          in_channels,
          dim=mask_dim,
          num_convs=mask_conv,
          kernel_dim=kernel_dim,
          act_cfg=act_cfg,
      )
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
  from torch.nn import init
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new BaseIAMDecoder object from inherited base classes: nn with example: obj = BaseIAMDecoder(in_channels, num_classes, ins_dim, ins_conv, mask_dim, mask_conv, kernel_dim, scale_factor, output_iam, num_masks, act_cfg)