class_docstr: Dilated Encoder for YOLOF <https://arxiv.org/abs/2103.09460>`.
  
  This module contains two types of components:
      - the original FPN lateral convolution layer and fpn convolution layer,
            which are 1x1 conv + 3x3 conv
      - the dilated residual block
  
  Args:
      in_channels (int): The number of input channels.
      out_channels (int): The number of output channels.
      block_mid_channels (int): The number of middle block output channels
      num_residual_blocks (int): The number of residual blocks.
      block_dilations (list): The list of residual blocks dilation.
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code: def __init__(
      self,
      in_channels,
      out_channels,
      block_mid_channels,
      num_residual_blocks,
      block_dilations,
  ):
      super(DilatedEncoder, self).__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.block_mid_channels = block_mid_channels
      self.num_residual_blocks = num_residual_blocks
      self.block_dilations = block_dilations
      self._init_layers()
  
file_path: mmdetection/mmdet/models/necks/dilated_encoder.py
imports: import torch.nn
  from mmcv.cnn import ConvModule, is_norm
  from mmengine.model import caffe2_xavier_init, constant_init, normal_init
  from torch.nn import BatchNorm2d
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new DilatedEncoder object from inherited base classes: nn with example: obj = DilatedEncoder(in_channels, out_channels, block_mid_channels, num_residual_blocks, block_dilations)