if (quant_specs_.weight_quantization) {
    patterns.add<TFDynamicRangeQuantization>(ctx, quant_params);
  } else {
    patterns.add<TFFullQuantization, TFFullQuantizationReverse>(ctx,
                                                                quant_params);
    patterns.add<QuantizeSameScaleOpsPattern>(ctx, GetTfQuantScaleSpec,
                                              target_opset_);
    patterns.add<QuantizeAvgPoolOpPattern>(ctx);
  }

        excludes.isEmpty() -> patternSpecFor(includes)
        else -> patternSpecFor(includes).and(patternSpecFor(excludes).negate())
    }


private
fun patternSpecFor(patterns: List<String>) =

include "mlir/Dialect/Func/IR/FuncOps.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

//===----------------------------------------------------------------------===//
// TensorList transformation patterns.
// Note that the pattern below rewrites `TensorList` tensors  (which has type DT_VARIANT)
// into regular tensors. We also assume that each element in the `TensorList` has
// a same constant shape.

     *
     * <p>The given pattern set is used to configure the filter. Only files which match the specified include patterns
     * will be included in the filtered tree. Any files which match the specified exclude patterns will be excluded from
     * the filtered tree.</p>
     *
     * @param patterns the pattern set to use to configure the filter.
     * @return The filtered tree.
     */

	}
	for _, tc := range testCases {
		a := splitRegexp(tc.pattern)
		if !reflect.DeepEqual(a, tc.result) {
			t.Errorf("splitRegexp(%q) = %#v; want %#v", tc.pattern, a, tc.result)
		}

		// If there is any error in the pattern, one of the returned subpatterns
		// needs to have an error as well.
		if _, err := regexp.Compile(tc.pattern); err != nil {
			ok := true

};

// Adds any canonicalization patterns to list of supported `patterns`.
// TODO(b/161726307): Move or import the relevant patterns to LowerTF pass and
// remove this.
void AddCanonicalizationPatterns(MLIRContext* context,
                                 RewritePatternSet* patterns) {
  for (auto op : context->getRegisteredOperations())
    op.getCanonicalizationPatterns(*patterns, context);
}

//
// The //go:embed directive accepts multiple space-separated patterns for
// brevity, but it can also be repeated, to avoid very long lines when there are
// many patterns. The patterns are interpreted relative to the package directory
// containing the source file. The path separator is a forward slash, even on
// Windows systems. Patterns may not contain ‘.’ or ‘..’ or empty path elements,

template <typename T>
class OperationPass;
class Pass;

namespace mhlo {

/// Converter to be used along with the fallback Tf2Xla patterns below.
class Tf2XlaTypeConverter : public TypeConverter {
 public:
  Tf2XlaTypeConverter();
};

/// Adds the TF to XLA via TF2XLA rewrite patterns to the pattern list.
/// `prefer_tf2xla` means an op will be included iff it is not in

      quant_specs};

  populateWithGenerated(patterns);

  if (quant_specs.weight_quantization || quant_specs.use_fake_quant_num_bits ||
      quant_specs.qdq_conversion_mode ==
          quant::QDQConversionMode::kQDQDynamic) {
    patterns.add<TFLDynamicRangeQuantization>(ctx, quant_params);
  } else {
    patterns.add<TFLFullQuantization, TFLFullQuantizationReverse>(ctx,

        when:
        select('more.than.two.dots')

        then:
        def ex = thrown(IllegalArgumentException)
        ex.message == "'more.than.two.dots' is an invalid pattern. Patterns should have one or two dots."
    }

    def "can use filters with two dots or fewer"() {
        when:
        select('one.dot', 'has.two.dots', 'ModuleName')

        then:
        noExceptionThrown()