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 forward_cpu(self, inputs):
      self.retain_inputs((0, 1))
      diff = (inputs[0] - inputs[1]).ravel()
      return (
          numpy.array(diff.dot(diff) / diff.size, dtype=diff.dtype),
      )
  
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 2 for calling the MeanSquaredError obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: numpy