code: def test_evaluate(self):
      N = 0
  
      class Model(eg.ModelBase):
          a: jnp.ndarray = eg.node()
  
          def init_step(
              self,
              key: jnp.ndarray,
              inputs: tp.Any,
          ) -> "Model":
              self.a = jnp.array(0, dtype=jnp.int32)
              return self
  
          def test_step(self, inputs, labels):
              nonlocal N
              N += 1
  
              preds = inputs + 1.0
              self.a += 1
  
              loss = 0.1
              logs = dict(loss=jnp.sum(inputs))
  
              return loss, logs, self
  
          def reset_metrics(self):
              pass
  
      model = Model()
  
      x = np.random.uniform(size=(100, 1))
  
      logs = model.evaluate(x, batch_size=100)
      assert np.allclose(logs["loss"], np.sum(x))
      assert N == 1
      assert model.a == 1
  
      logs = model.evaluate(x, batch_size=50)
      assert np.allclose(logs["loss"], np.sum(x[50:]))
      assert N == 2
      assert model.a == 3
  
      logs = model.evaluate(x, batch_size=50)
      assert np.allclose(logs["loss"], np.sum(x[50:]))
      assert N == 2
      assert model.a == 5
  
      model = model.eager()
  
      logs = model.evaluate(x, batch_size=50)
      assert np.allclose(logs["loss"], np.sum(x[50:]))
      assert N == 4
      assert model.a == 7
  
file_path: elegy/tests/model/model_base_test.py
imports: import typing
  import unittest
  import jax.numpy
  import numpy
  import torch
  from torch.utils.data import DataLoader, TensorDataset
  import elegy
label_desc: Use image node_id 2 for calling the TestModelBase obj's underlying member method code with example usage: obj.test_evaluate() and returns: self, loss,  logs,  self