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);
  }

};

// 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,

  target.addLegalDialect<vhlo::VhloDialect>();

  StablehloToOdmlTypeConverter converter;
  RewritePatternSet patterns(context);
  stablehlo::populateStablehloToVhloPatterns(&patterns, &converter, context);

  if (failed(applyPartialConversion(module, target, std::move(patterns)))) {
    return module->emitError("Failed partial conversion to VHLO");
  }
  return success();
}

    public AbstractCopyTask filesMatching(String pattern, Action<? super FileCopyDetails> action) {
        getMainSpec().filesMatching(pattern, action);
        return this;
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public AbstractCopyTask filesMatching(Iterable<String> patterns, Action<? super FileCopyDetails> action) {
        getMainSpec().filesMatching(patterns, action);
        return this;
    }

void AddQuantizationUnitLocPass::runOnOperation() {
  MLIRContext* ctx = &getContext();
  RewritePatternSet patterns(ctx);
  func::FuncOp func = getOperation();

  patterns.add<AddQuantizationUnitLoc>(ctx);
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    func.emitError() << "quant-add-quantization-unit-loc pattern "
                        "conversion did not converge.";
    signalPassFailure();
  }
}

  }

  RewritePatternSet patterns(&getContext());
  auto func = getOperation();
  auto* ctx = func.getContext();
  TFL::populateWithGenerated(patterns);
  patterns.add<quant::FoldTrivalRequantizeOp<QuantizeOp>>(ctx);
  patterns.add<PruneUnusedOpsWithSideEffect<TFL::LSTMOp>>(ctx);
  patterns.add<PruneUnusedOpsWithSideEffect<TFL::UnidirectionalSequenceLSTMOp>>(
      ctx);

void ConvertTFQuantTypes::runOnOperation() {
  TFQuantTypeConverter converter;
  RewritePatternSet patterns(&getContext());
  patterns.add<TFQuantTypePattern>(&getContext(), converter);
  patterns.add<TFConstOpQuantToIntPattern, TFUniformQuantizedOpsPattern>(
      &getContext());
  populateFunctionOpInterfaceTypeConversionPattern<func::FuncOp>(patterns,
                                                                 converter);