code: def test_invalid_settings(
      input_dim,
      output_dim,
      hidden_sizes,
      n_heads,
      nonlinearity,
      w_init,
      b_init,
  ):
      """Test Multi-headed MLPModule with invalid parameters.
  
      Args:
          input_dim (int): Input dimension.
          output_dim (int): Ouput dimension.
          hidden_sizes (list[int]): Size of hidden layers.
          n_heads (int): Number of output layers.
          nonlinearity (callable or torch.nn.Module): Non-linear functions for
              output layers
          w_init (list[callable]): Initializer function for the weight in
              output layer.
          b_init (list[callable]): Initializer function for the bias in
              output layer.
  
      """
      expected_msg_template = (
          "should be either an integer or a collection of "
          "length n_heads"
      )
      with pytest.raises(ValueError, match=expected_msg_template):
          MultiHeadedMLPModule(
              n_heads=n_heads,
              input_dim=input_dim,
              output_dims=output_dim,
              hidden_sizes=hidden_sizes,
              hidden_nonlinearity=None,
              hidden_w_init=nn.init.ones_,
              output_nonlinearities=nonlinearity,
              output_w_inits=list(map(_helper_make_inits, w_init)),
              output_b_inits=b_init,
          )
  
imports: import pytest
  import torch
  from torch import nn
  from garage.torch.modules import MultiHeadedMLPModule
label_desc: Use image node_id 4 for calling a global function with example usage: test_invalid_settings(input_dim, output_dim, hidden_sizes, n_heads, nonlinearity, w_init, b_init) without return types