class_docstr: Implements a deterministic policy network.
  
  The policy network selects action based on the state of the environment.
  It uses a PyTorch neural network module to fit the function of pi(s).
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docstr: Compute actions from the observations.
  
  Args:
      observations (torch.Tensor): Batch of observations on default
          torch device.
  
  Returns:
      torch.Tensor: Batch of actions.
docstr_tok: ["Compute","actions","from","the","observations",".","Args",":","observations","(","torch.Tensor",")",":","Batch","of","observations","on","default","torch","device",".","Returns",":","torch.Tensor",":","Batch","of","actions","."]
code: def forward(self, observations):
      """Compute actions from the observations.
  
      Args:
          observations (torch.Tensor): Batch of observations on default
              torch device.
  
      Returns:
          torch.Tensor: Batch of actions.
      """
      return self._module(observations)
  
file_path: garage/src/garage/torch/policies/deterministic_mlp_policy.py
imports: import akro
  import numpy
  import torch
  from garage.torch import global_device
  from garage.torch.modules import MLPModule
  from garage.torch.policies.policy import Policy
label_desc: Use image node_id 2 for calling the DeterministicMLPPolicy obj's underlying member method code with example usage: obj.forward(observations) and returns: self