code: 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
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neural_network/_base.py
imports: import numpy
  from ..utils.fixes import expit
label_desc: Use image node_id 10 for calling a global function with example usage: squared_loss(y_true, y_pred) and returns: unknown