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.
docstr: Forward method.
  
  Args:
      input_value (torch.Tensor): Input values with (N, *, input_dim)
          shape.
  
  Returns:
      torch.Tensor: Output value
docstr_tok: ["Forward","method",".","Args",":","input_value","(","torch.Tensor",")",":","Input","values","with","(","N",",","*",",","input_dim",")","shape",".","Returns",":","torch.Tensor",":","Output","value"]
code: def forward(self, input_value):
      """Forward method.
  
      Args:
          input_value (torch.Tensor): Input values with (N, *, input_dim)
              shape.
  
      Returns:
          torch.Tensor: Output value
  
      """
      return super().forward(input_value)[0]
  
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 2 for calling the MLPModule obj's underlying member method code with example usage: obj.forward(input_value) and returns: super