docstr: Test mean squared error on x and y.
docstr_tok: ["Test","mean","squared","error","on","x","and","y","."]
code: def test_on_mean_squared_log_error(self):
      """Test mean squared error on x and y."""
      x = jn.array([3, 5, 2, 7], dtype=jn.float32)
      y = jn.array([2, 5, 4, 8], dtype=jn.float32)
      output = objax.functional.loss.mean_squared_log_error(x, y)
      expected = jn.array(
          [0.08276097, 0.0, 0.26094282, 0.013872852], dtype=jn.float32
      )
      self.assertAlmostEqual(
          jn.abs(output - expected).sum(), 0, delta=ERROR_TOLERANCE
      )
      # Test mean squared error on x and y with keep_axis
      x = jn.array([[3, 5, 2.5, 7]], dtype=jn.float32)
      y = jn.array([[2.5, 5, 4, 8]], dtype=jn.float32)
      output = objax.functional.loss.mean_squared_log_error(
          x, y, keep_axis=(0,)
      )
      expected = jn.array([0.039730143], dtype=jn.float32)
      self.assertAlmostEqual(
          jn.abs(output - expected).sum(), 0, delta=ERROR_TOLERANCE
      )
      # Test mean squared error on x and y with no `keep_axis`
      x = jn.array(
          [[0.1152, 0.6301, 0.6845], [0.0958, 0.2180, 0.8503]],
          dtype=jn.float32,
      )
      y = jn.array(
          [[0.0026, 0.9501, 0.1955], [0.3537, 0.1689, 0.7774]],
          dtype=jn.float32,
      )
      output = objax.functional.loss.mean_squared_log_error(
          x, y, keep_axis=()
      )
      expected = jn.array(0.03483658, dtype=jn.float32)
      self.assertAlmostEqual(
          jn.abs(output - expected).sum(), 0, delta=ERROR_TOLERANCE
      )
  
file_path: objax/tests/loss.py
imports: import unittest
  import jax.numpy
  import objax
label_desc: Use image node_id 3 for calling the TestLoss obj's underlying member method code with example usage: obj.test_on_mean_squared_log_error() without return types