code: def conv2d(
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      bias_attr=False,
      lr_mult=1.0,
      **kwargs,
  ):
      assert bias_attr in [
          True,
          False,
      ], "bias_attr should be True or False"
      weight_attr = paddle.ParamAttr(learning_rate=lr_mult)
      if bias_attr:
          bias_attr = paddle.ParamAttr(learning_rate=lr_mult)
      return nn.Conv2D(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          weight_attr=weight_attr,
          bias_attr=bias_attr,
          **kwargs,
      )
  
imports: from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import DropPath, Identity
  from paddleseg.cvlibs.param_init import constant_init, kaiming_normal_init, trunc_normal_init
label_desc: Use image node_id 1 for calling a global function with example usage: conv2d(in_channels, out_channels, kernel_size, stride, padding, bias_attr, lr_mult) and returns: nn