Fix the l2norm zip tests and re-enable all the fully_quantize tests through the new converter

PiperOrigin-RevId: 314469215
Change-Id: I6dfde69466270ec9ea6bdbef07c5aba45651cfd6

tensorflow/lite/testing/op_tests/l2norm.py

@@ -35,9 +35,14 @@ def make_l2norm_tests(options):
       "epsilon": [None, 1e-12, 1e-3],
       "fully_quantize": [False],
   }, {
-      "input_shape": [[5, 7], [1, 1, 1, 1], [1, 3, 4, 3], [3, 15, 14, 3]],
-      "dim": [0, 1, 2, 3, [2, 3], -2],
-      "epsilon": [None, 1e-12, 1e-3],
+      "input_shape": [[1, 1, 1, 1], [1, 3, 4, 3], [3, 15, 14, 3]],
+      "dim": [3],
+      "epsilon": [None, 1e-12],
+      "fully_quantize": [True],
+  }, {  # use another group of test so the dim is set to fuse to tfl.l2norm
+      "input_shape": [[5, 7]],
+      "dim": [1],
+      "epsilon": [None, 1e-12],
       "fully_quantize": [True],
   }]
 
@@ -62,4 +67,4 @@ def make_l2norm_tests(options):
       test_parameters,
       build_graph,
       build_inputs,
-      expected_tf_failures=18)
+      expected_tf_failures=9)

tensorflow/lite/testing/toco_convert.py

@@ -115,13 +115,6 @@ def toco_convert(options, graph_def, input_tensors, output_tensors, **kwargs):
       converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
           graphdef_file.name, input_arrays, output_tensors, input_shapes)
 
-      # TODO(b/145313371): Evaluate should we make it work with the new
-      # converter.
-      # Note: Currently this line is a non-functional change because the new
-      # converter is disabled by default. Since this code path doesn't work
-      # with new converter yet, it's explicitly disabled for easier testing.
-      converter.experimental_new_converter = False
-
       def representative_dataset(input_tensors):
         calibration_inputs = []
         for _, shape, _ in input_tensors: