code: def conv(
      in_channels=64,
      out_channels=64,
      kernel_size=3,
      stride=1,
      padding=1,
      bias=True,
      mode="CBR",
      negative_slope=0.2,
  ):
      L = []
      for t in mode:
          if t == "C":
              L.append(
                  nn.Conv2D(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      kernel_size=kernel_size,
                      stride=stride,
                      padding=padding,
                      bias_attr=bias,
                  )
              )
          elif t == "T":
              L.append(
                  nn.Conv2DTranspose(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      kernel_size=kernel_size,
                      stride=stride,
                      padding=padding,
                      bias_attr=bias,
                  )
              )
          elif t == "B":
              L.append(
                  nn.BatchNorm2D(
                      out_channels, momentum=0.9, eps=1e-04, affine=True
                  )
              )
          elif t == "I":
              L.append(nn.InstanceNorm2D(out_channels, affine=True))
          elif t == "R":
              L.append(nn.ReLU())
          elif t == "r":
              L.append(nn.ReLU())
          elif t == "L":
              L.append(nn.LeakyReLU(negative_slope=negative_slope))
          elif t == "l":
              L.append(nn.LeakyReLU(negative_slope=negative_slope))
          elif t == "2":
              L.append(nn.PixelShuffle(upscale_factor=2))
          elif t == "3":
              L.append(nn.PixelShuffle(upscale_factor=3))
          elif t == "4":
              L.append(nn.PixelShuffle(upscale_factor=4))
          elif t == "U":
              L.append(nn.Upsample(scale_factor=2, mode="nearest"))
          elif t == "u":
              L.append(nn.Upsample(scale_factor=3, mode="nearest"))
          elif t == "v":
              L.append(nn.Upsample(scale_factor=4, mode="nearest"))
          elif t == "M":
              L.append(
                  nn.MaxPool2D(
                      kernel_size=kernel_size, stride=stride, padding=0
                  )
              )
          elif t == "A":
              L.append(
                  nn.AvgPool2D(
                      kernel_size=kernel_size, stride=stride, padding=0
                  )
              )
          else:
              raise NotImplementedError("Undefined type: ".format(t))
      return sequential(*L)
  
imports: from collections import OrderedDict
  import numpy
  import paddle.nn
label_desc: Use image node_id 2 for calling a global function with example usage: conv(in_channels, out_channels, kernel_size, stride, padding, bias, mode, negative_slope) and returns: sequential