class_docstr: Base class for all baselines.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      name (str): Value function name, also the variable scope.
class_docstr_tok: ["Base","class","for","all","baselines",".","Args",":","env_spec","(","EnvSpec",")",":","Environment","specification",".","name","(","str",")",":","Value","function","name",",","also","the","variable","scope","."]
docstr: Compute mean value of loss.
  
  Args:
      obs (torch.Tensor): Observation from the environment
          with shape :math:`(N \dot [T], O*)`.
      returns (torch.Tensor): Acquired returns with shape :math:`(N, )`.
  
  Returns:
      torch.Tensor: Calculated negative mean scalar value of
          objective (float).
docstr_tok: ["Compute","mean","value","of","loss",".","Args",":","obs","(","torch.Tensor",")",":","Observation","from","the","environment","with","shape",":","math",":","`","(","N","\\dot","[","T","]",",","O","*",")","`",".","returns","(","torch.Tensor",")",":","Acquired","returns","with","shape",":","math",":","`","(","N",",",")","`",".","Returns",":","torch.Tensor",":","Calculated","negative","mean","scalar","value","of","objective","(","float",")","."]
code: def compute_loss(self, obs, returns):
      r"""Compute mean value of loss.
  
      Args:
          obs (torch.Tensor): Observation from the environment
              with shape :math:`(N \dot [T], O*)`.
          returns (torch.Tensor): Acquired returns with shape :math:`(N, )`.
  
      Returns:
          torch.Tensor: Calculated negative mean scalar value of
              objective (float).
  
      """
  
imports: import abc
  import torch.nn
label_desc: Use image node_id 2 for calling the ValueFunction obj's underlying member method code with example usage: obj.compute_loss(obs, returns) without return types