code: def __init__(
      self,
      dim,
      num_heads=8,
      HW=[8, 25],
      local_k=[3, 3],
  ):
      super().__init__()
      self.HW = HW
      self.dim = dim
      self.local_mixer = nn.Conv2D(
          dim,
          dim,
          local_k,
          1,
          [local_k[0] // 2, local_k[1] // 2],
          groups=num_heads,
          weight_attr=ParamAttr(initializer=KaimingNormal()),
      )
  
imports: from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
label_desc: Use image node_id 1 to create a new ConvMixer object from inherited base classes: nn with example: obj = ConvMixer(dim, num_heads, HW, local_k)