parameters: (self, params, payoff_matrices)
return_statement: return self.gradients(*params, payoff_matrices, self.p, self.proj_grad)
docstring: Compute and return gradients (and exploitabilities) for all parameters.
  
      Args:
        params: tuple of params (dist, y), see ate.gradients
        payoff_matrices: (>=2 x A x A) np.array, payoffs for each joint action
      Returns:
        tuple of gradients (grad_dist, grad_y), see ate.gradients
        unregularized exploitability (stochastic estimate)
        tsallis regularized exploitability (stochastic estimate)
docstring_summary: Compute and return gradients (and exploitabilities) for all parameters.
docstring_tokens: Compute and return gradients ( and exploitabilities ) for all parameters .
function: def compute_gradients(self, params, payoff_matrices):
      """Compute and return gradients (and exploitabilities) for all parameters.
  
      Args:
        params: tuple of params (dist, y), see ate.gradients
        payoff_matrices: (>=2 x A x A) np.array, payoffs for each joint action
      Returns:
        tuple of gradients (grad_dist, grad_y), see ate.gradients
        unregularized exploitability (stochastic estimate)
        tsallis regularized exploitability (stochastic estimate)
      """
      return self.gradients(*params, payoff_matrices, self.p, self.proj_grad)
function_tokens: def compute_gradients ( self , params , payoff_matrices ) : return self . gradients ( * params , payoff_matrices , self . p , self . proj_grad )