class_docstr: Upsample block for DRRG and TextSnake.
  
  DRRG: `Deep Relational Reasoning Graph Network for Arbitrary Shape
  Text Detection <https://arxiv.org/abs/2003.07493>`_.
  
  TextSnake: `A Flexible Representation for Detecting Text of Arbitrary
  Shapes <https://arxiv.org/abs/1807.01544>`_.
  
  Args:
      in_channels (list[int]): Number of input channels at each scale. The
          length of the list should be 4.
      out_channels (int): The number of output channels.
      init_cfg (dict or list[dict], optional): Initialization configs.
          Defaults to None.
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      init_cfg: Optional[Union[Dict, List[Dict]]] = None,
  ) -> None:
      super().__init__(init_cfg=init_cfg)
  
      assert isinstance(in_channels, int)
      assert isinstance(out_channels, int)
  
      self.conv1x1 = nn.Conv2d(
          in_channels, in_channels, kernel_size=1, stride=1, padding=0
      )
      self.conv3x3 = nn.Conv2d(
          in_channels, out_channels, kernel_size=3, stride=1, padding=1
      )
      self.deconv = nn.ConvTranspose2d(
          out_channels, out_channels, kernel_size=4, stride=2, padding=1
      )
  
file_path: mmocr/mmocr/models/textdet/necks/fpn_unet.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from mmengine.model import BaseModule
  from torch import nn
  from mmocr.registry import MODELS
label_desc: Use image node_id 1 to create a new UpBlock object from inherited base classes: BaseModule with example: obj = UpBlock(in_channels, out_channels, init_cfg)