class_docstr: Mean squared error (a.k.a. Euclidean loss) function.
class_docstr_tok: ["Mean","squared","error","(","a.k.a",".","Euclidean","loss",")","function","."]
code: def backward(self, indexes, gy):
      x0, x1 = self.get_retained_inputs()
      ret = []
      diff = x0 - x1
      gy0 = chainer.functions.broadcast_to(gy[0], diff.shape)
      gx0 = gy0 * diff * (2.0 / diff.size)
      if 0 in indexes:
          ret.append(gx0)
      if 1 in indexes:
          ret.append(-gx0)
      return ret
  
file_path: chainer/chainer/functions/loss/mean_squared_error.py
imports: import numpy
  from chainer import function_node
  import chainer.functions
  from chainer.utils import type_check
label_desc: Use image node_id 4 for calling the MeanSquaredError obj's underlying member method code with example usage: obj.backward(indexes, gy) and returns: ret