# tensorflow/core/api_def/base_api/*
# {op_name: {API's attribute name: OpDef's attribute name}}
_ATTRIBUTE_RENAMES = {
    'Mean': {'axis': 'reduction_indices'},
    'Split': {'axis': 'split_dim'},
    'SplitV': {'axis': 'split_dim'},
}


def _get_type_from_proto(arg_def=None, attr_def=None):
  if not arg_def:
    if attr_def.type == 'bool':
      return TFRTypes.I1

      "BroadcastArgs", "BroadcastGradientArgs", "OneHot", "Concat", "ConcatV2",
      "ConcatOffset", "Const", "MirrorPad", "MirrorPadGrad", "Pack", "Pad",
      "PadV2", "Reverse", "ReverseV2", "ReverseSequence", "Slice", "Split",
      "SplitV", "StridedSlice", "StridedSliceGrad",
      "ResourceStridedSliceAssign", "Tile", "Transpose", "InvertPermutation",
      "Unpack", "DeviceIndex", "TensorStridedSliceUpdate", "XlaConcatND",
      "XlaSplitND",

   *                    v
   */
  // Define
  TF_Operation* feed = Placeholder(func_graph_, s_);
  TF_Operation* split = Split3(feed, func_graph_, s_);
  TF_Operation* add = Add({split, 0}, {split, 2}, func_graph_, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  DefineT(1, {add}, {{split, 0}, {split, 2}}, {{add, 0}}, {});

  // Use, run, and verify
  TF_Operation* two = ScalarConst(2, host_graph_, s_, "two");

	for _, tt := range splittests {
		a := SplitN(tt.s, tt.sep, tt.n)
		if !eq(a, tt.a) {
			t.Errorf("Split(%q, %q, %d) = %v; want %v", tt.s, tt.sep, tt.n, a, tt.a)
			continue
		}
		if tt.n == 0 {
			continue
		}
		s := Join(a, tt.sep)
		if s != tt.s {
			t.Errorf("Join(Split(%q, %q, %d), %q) = %q", tt.s, tt.sep, tt.n, tt.sep, s)
		}
		if tt.n < 0 {
			b := Split(tt.s, tt.sep)
			if !reflect.DeepEqual(a, b) {

        if (params == null) {
            params = split(getCrawlerMetadataNameMapping(), "\n").get(stream -> stream.filter(StringUtil::isNotBlank).map(v -> {
                final String[] values = v.split("=");
                if (values.length == 2) {
                    final String[] subValues = values[1].split(":", 3);
                    if (subValues.length == 3) {

		parentResources[r.Name] = len(apiResourceList) - 1
	}

	// Loop through all subresources
	for _, r := range list {
		// Split resource name and subresource name
		split := strings.SplitN(r.Name, "/", 2)

		if len(split) != 2 {
			// Skip parent resources
			continue
		}

		var scope apidiscoveryv2.ResourceScope
		if r.Namespaced {
			scope = apidiscoveryv2.ScopeNamespace

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-unroll-batch-matmul %s | FileCheck %s

//==== V1 tests ====

func.func @batchMatMulTwoDim(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) : (tensor<2x3x4x5xf32>, tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32>
  func.return %0 : tensor<2x3x4x6xf32>

  // CHECK-LABEL: batchMatMulTwoDim

  python_paths = []
  if environ_cp.get('PYTHONPATH'):
    python_paths = environ_cp.get('PYTHONPATH').split(':')
  try:
    stderr = open(os.devnull, 'wb')
    library_paths = run_shell([
        python_bin_path, '-c',
        'import site; print("\\n".join(site.getsitepackages()))'
    ],
                              stderr=stderr).split('\n')
  except subprocess.CalledProcessError:
    library_paths = [
        run_shell([

		}

		s := Join(a, []byte(tt.sep))
		if string(s) != tt.s {
			t.Errorf(`Join(Split(%q, %q, %d), %q) = %q`, tt.s, tt.sep, tt.n, tt.sep, s)
		}
		if tt.n < 0 {
			b := Split([]byte(tt.s), []byte(tt.sep))
			if !reflect.DeepEqual(a, b) {
				t.Errorf("Split disagrees withSplitN(%q, %q, %d) = %v; want %v", tt.s, tt.sep, tt.n, b, a)
			}
		}
		if len(a) > 0 {
			in, out := a[0], s

            replaceConfigurationWildcards(md);
            for (String s : conf) {
                String[] ops = s.split("->");
                if (ops.length == 1) {
                    String[] modConfs = ops[0].split(",");
                    if (!useDefaultMappingToGuessRightOperand) {
                        for (String modConf : modConfs) {