class_docstr: Deconvolution upsample module in decoder for UNet (2X upsample).
  
  This module uses deconvolution to upsample feature map in the decoder
  of UNet.
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
      with_cp (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed. Default: False.
      norm_cfg (dict | None): Config dict for normalization layer.
          Default: dict(type='BN').
      act_cfg (dict | None): Config dict for activation layer in ConvModule.
          Default: dict(type='ReLU').
      kernel_size (int): Kernel size of the convolutional layer. Default: 4.
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docstr: Forward function.
docstr_tok: ["Forward","function","."]
code: def forward(self, x):
      """Forward function."""
  
      if self.with_cp and x.requires_grad:
          out = cp.checkpoint(self.deconv_upsamping, x)
      else:
          out = self.deconv_upsamping(x)
      return out
  
imports: import torch.nn
  import torch.utils.checkpoint
  from annotator.uniformer.mmcv.cnn import UPSAMPLE_LAYERS, ConvModule, build_activation_layer, build_norm_layer, constant_init, kaiming_init
  from annotator.uniformer.mmcv.runner import load_checkpoint
  from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
  from annotator.uniformer.mmseg.utils import get_root_logger
  from ..builder import BACKBONES
  from ..utils import UpConvBlock
label_desc: Use image node_id 2 for calling the DeconvModule obj's underlying member method code with example usage: obj.forward(x) and returns: out