code: def test_critic(input_dim, hidden_dim):
      """Tests if the critic fully connected hidden layers have the expected shapes
      and if the output shape is 1."""
      x = torch.randn(1, input_dim)
      model = Critic(hidden_dim=hidden_dim)
  
      output = model(x)
      assert output.shape[-1] == 1
  
      layers = list(model.children())
      w1, b1 = layers[0].weight, layers[0].bias
      assert w1.shape == (hidden_dim, input_dim)
      assert b1.shape == (hidden_dim,)
  
      w2, b2 = layers[2].weight, layers[2].bias
      assert w2.shape == (hidden_dim, hidden_dim)
      assert b2.shape == (hidden_dim,)
  
      w3, b3 = layers[4].weight, layers[4].bias
      assert w3.shape == (1, hidden_dim)
      assert b3.shape == (1,)
  
imports: import pytest
  import torch
  from alibi.models.pytorch.actor_critic import Actor, Critic
label_desc: Use image node_id 2 for calling a global function with example usage: test_critic(input_dim, hidden_dim) without return types