parameters: (y_true, y_pred)
return_statement: return ((y_true - y_pred) ** 2).mean() / 2
docstring: Compute the squared loss for regression.
  
      Parameters
      ----------
      y_true : array-like or label indicator matrix
          Ground truth (correct) values.
  
      y_pred : array-like or label indicator matrix
          Predicted values, as returned by a regression estimator.
  
      Returns
      -------
      loss : float
          The degree to which the samples are correctly predicted.
docstring_summary: Compute the squared loss for regression.
docstring_tokens: Compute the squared loss for regression .
function: def squared_loss(y_true, y_pred):
      """Compute the squared loss for regression.
  
      Parameters
      ----------
      y_true : array-like or label indicator matrix
          Ground truth (correct) values.
  
      y_pred : array-like or label indicator matrix
          Predicted values, as returned by a regression estimator.
  
      Returns
      -------
      loss : float
          The degree to which the samples are correctly predicted.
      """
      return ((y_true - y_pred) ** 2).mean() / 2
function_tokens: def squared_loss ( y_true , y_pred ) : return ( ( y_true - y_pred ) ** 2 ) . mean ( ) / 2