code: def test_weighted_skip_multilayer_perceptron():
      "Test for weighted skip connection from the input to the output"
      seed = 123
      torch.manual_seed(seed)
      dim = 1
      features = torch.Tensor(
          [[0.8343], [1.2713], [1.2713], [1.2713], [1.2713]]
      )
      layer = dc.models.torch_models.layers.MultilayerPerceptron(
          d_input=dim,
          d_hidden=(dim,),
          d_output=dim,
          activation_fn="silu",
          skip_connection=True,
          weighted_skip=False,
      )
      output = layer(features)
      output = output.detach().numpy()
      result = np.array(
          [[1.1032], [1.5598], [1.5598], [1.5598], [1.5598]]
      )
      assert np.allclose(output, result, atol=1e-04)
      assert output.shape == (5, 1)
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 33 for calling a global function with example usage: test_weighted_skip_multilayer_perceptron() without return types