code: def test_cond_conv2d(self):
      in_channels = 32
      out_channels = 64
      kernel_size = 3
      stride = 1
      padding = "same"
      num_experts = 5
  
      module = CondConv2d(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          num_experts=num_experts,
      )
      routing_weights = torch.FloatTensor(
          [0.2, 0.2, 0.2, 0.2, 0.2]
      ).reshape((1, 5))
      x = torch.FloatTensor(1, 32, 224, 224)
      output = module(x, routing_weights)
      gt_output_shape = torch.Size((1, 64, 224, 224))
      self.assertTrue(output.shape == gt_output_shape)
  
imports: import unittest
  import torch
  from towhee.models.layers.cond_conv2d import CondConv2d
label_desc: Use image node_id 1 for calling the CondConv2dTest obj's underlying member method code with example usage: obj.test_cond_conv2d() without return types