class_docstr: MLP Model.
  
  A Pytorch module composed only of a multi-layer perceptron (MLP), which
  maps real-valued inputs to real-valued outputs.
  
  Args:
      input_dim (int) : Dimension of the network input.
      output_dim (int): Dimension of the network output.
      hidden_sizes (list[int]): Output dimension of dense layer(s).
          For example, (32, 32) means this MLP consists of two
          hidden layers, each with 32 hidden units.
      hidden_nonlinearity (callable or torch.nn.Module): Activation function
          for intermediate dense layer(s). It should return a torch.Tensor.
          Set it to None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          torch.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          torch.Tensor.
      output_nonlinearity (callable or torch.nn.Module): Activation function
          for output dense layer. It should return a torch.Tensor.
          Set it to None to maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          torch.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          torch.Tensor.
      layer_normalization (bool): Bool for using layer normalization or not.
code: def __init__(
      self,
      input_dim,
      output_dim,
      hidden_sizes,
      hidden_nonlinearity=F.relu,
      hidden_w_init=nn.init.xavier_normal_,
      hidden_b_init=nn.init.zeros_,
      output_nonlinearity=None,
      output_w_init=nn.init.xavier_normal_,
      output_b_init=nn.init.zeros_,
      layer_normalization=False,
  ):
      super().__init__(
          1,
          input_dim,
          output_dim,
          hidden_sizes,
          hidden_nonlinearity,
          hidden_w_init,
          hidden_b_init,
          output_nonlinearity,
          output_w_init,
          output_b_init,
          layer_normalization,
      )
  
      self._output_dim = output_dim
  
imports: from torch import nn
  from torch.nn import functional
  from garage.torch.modules.multi_headed_mlp_module import MultiHeadedMLPModule
label_desc: Use image node_id 1 to create a new MLPModule object from inherited base classes: MultiHeadedMLPModule with example: obj = MLPModule(input_dim, output_dim, hidden_sizes, hidden_nonlinearity, hidden_w_init, hidden_b_init, output_nonlinearity, output_w_init, output_b_init, layer_normalization)