code: def __init__(
      self,
      in_chan,
      out_chan,
      kernel_size,
      stride,
      padding,
      dilation,
      norm_type="bN",
      activation="leaky_relu",
      embed_dim=None,
      complex_time_embedding=False,
      temb_layers=1,
      temb_activation="silu",
  ):
      super().__init__()
  
      self.in_chan = in_chan
      self.out_chan = out_chan
      self.kernel_size = kernel_size
      self.stride = stride
      self.padding = padding
      self.dilation = dilation
      self.temb_layers = temb_layers
      self.temb_activation = temb_activation
      self.complex_time_embedding = complex_time_embedding
  
      self.conv = ComplexConv2d(
          in_chan,
          out_chan,
          kernel_size,
          stride,
          padding,
          bias=norm_type is None,
          dilation=dilation,
      )
      self.norm = _get_norm(norm_type)(out_chan)
      self.activation = OnReIm(get_activation(activation))
      self.embed_dim = embed_dim
      if self.embed_dim is not None:
          ops = []
          for _ in range(max(0, self.temb_layers - 1)):
              ops += [
                  ComplexLinear(
                      self.embed_dim,
                      self.embed_dim,
                      complex_valued=True,
                  ),
                  OnReIm(get_activation(self.temb_activation)),
              ]
          ops += [
              FeatureMapDense(
                  self.embed_dim, self.out_chan, complex_valued=True
              ),
              OnReIm(get_activation(self.temb_activation)),
          ]
          self.embed_layer = nn.Sequential(*ops)
  
imports: import functools
  from functools import partial
  import numpy
  import torch
  from torch import Tensor, nn
  from torch.nn.modules.batchnorm import _BatchNorm
label_desc: Use image node_id 1 to create a new DCUNetComplexEncoderBlock object from inherited base classes: nn with example: obj = DCUNetComplexEncoderBlock(in_chan, out_chan, kernel_size, stride, padding, dilation, norm_type, activation, embed_dim, complex_time_embedding, temb_layers, temb_activation)