code: def _get_loss(self, loss_name: str):
      if loss_name == "logistic":
          x = TrainVar(jn.zeros(2))
          model_vars = VarCollection({"x": x})
  
          def loss():
              return jn.log(jn.exp(-jn.sum(x.value)) + 1)
  
          return model_vars, loss
      if loss_name == "square":
          # loss = x*x + y*y.
          x = TrainVar(jn.ones(2))
          y = TrainVar(jn.ones(3))
          model_vars = VarCollection({"x": x, "y": y})
  
          def loss():
              return jn.dot(x.value, x.value) + jn.dot(y.value, y.value)
  
          return model_vars, loss
      if loss_name == "rastrigin":
          d = 2
          x = TrainVar(jn.ones(d))
          model_vars = VarCollection({"x": x})
  
          def loss():
              return (
                  10 * d
                  + jn.dot(x.value, x.value)
                  - 10 * jn.sum(jn.cos(2 * math.pi * x.value))
              )
  
          return model_vars, loss
      raise ValueError
  
imports: import math
  import unittest
  import jax.numpy
  import numpy
  import objax
  from objax.variable import TrainVar, VarCollection
label_desc: Use image node_id 3 for calling the TestOptimizers obj's underlying member method code with example usage: obj._get_loss(loss_name) and returns: model_vars,  loss, model_vars,  loss, model_vars,  loss, jn, unknown, unknown