def TFL_BroadcastableBinaryBuilder :
  OpBuilder<(ins "Value":$lhs, "Value":$rhs),
  [{
    auto resultType =
      OpTrait::util::getBroadcastedType(lhs.getType(), rhs.getType());
    if (!resultType)
      mlir::emitError($_state.location, "non-broadcastable operands");
    $_state.addOperands({lhs, rhs});
    $_state.types.push_back(resultType);
  }]>;

def TFL_FusedBroadcastableBinaryBuilder :

      RankedTensorType::get({shape_rank}, rewriter.getIntegerType(64));
  Type resultType = RankedTensorType::get(shape, element_type);
  auto constant_attr = DenseElementsAttr::get(shape_spec_type, shape);
  auto shape_tensor =
      rewriter.create<TF::ConstOp>(loc, shape_spec_type, constant_attr);
  return rewriter.create<TF::ReshapeOp>(loc, resultType, /*tensor=*/value,
                                        /*shape=*/shape_tensor);
}

	type Analyzer struct {
		Name             string
		Doc              string
		Flags            flag.FlagSet
		Run              func(*Pass) (interface{}, error)
		RunDespiteErrors bool
		ResultType       reflect.Type
		Requires         []*Analyzer
		FactTypes        []Fact
	}

The Flags field declares a set of named (global) flag variables that
control analysis behavior. Unlike vet, analysis flags are not declared

    const double scale, int64_t zero_point, int64_t storage_min,
    int64_t storage_max) {
  const auto result_type = cast<ShapedType>(tensor_type);
  if (!isa<FloatType>(result_type.getElementType())) return {};
  Builder builder(result_type.getContext());

  // Only support 8-bits and 16-bits
  if (bit_width != 8 && bit_width != 16) return {};

    if (IsStringType(operand_type)) return true;
  }
  return false;
}

bool HasStringResult(Operation& op) {
  for (auto result : op.getResults()) {
    auto result_type = getElementTypeOrSelf(result);
    if (IsStringType(result_type)) return true;
  }
  return false;
}

bool MatchesPattern(Operation& op,
                    const llvm::DenseSet<OperationName>& supported_ops) {

import org.gradle.reporting.ReportRenderer;
import org.gradle.util.internal.GFileUtils;

import java.io.File;
import java.io.IOException;
import java.util.List;

import static org.gradle.api.tasks.testing.TestResult.ResultType.SKIPPED;

public class DefaultTestReport implements TestReporter {
    private final BuildOperationRunner buildOperationRunner;
    private final BuildOperationExecutor buildOperationExecutor;

}  // namespace internal
}  // namespace testing

#define GTEST_MESSAGE_AT_(file, line, message, result_type) \
  ::testing::internal::AssertHelper(result_type, file, line, message) \
    = ::testing::Message()

#define GTEST_MESSAGE_(message, result_type) \
  GTEST_MESSAGE_AT_(__FILE__, __LINE__, message, result_type)

#define GTEST_FATAL_FAILURE_(message) \

      output_types.reserve(op_with_region->getNumResults());
      for (const Value result : op_with_region->getResults()) {
        const Type result_type = result.getType();
        if (mlir::isa<NoneType>(result_type)) {
          outputs_replaced.push_back(result);
          output_types.push_back(result_type);
          continue;
        }
        const Type result_element_type =

    // Sometimes the result type is more specific than what the reshape builder
    // can infer.
    auto result_type = op.getResult().getType();
    rewriter.replaceOpWithNewOp<ReshapeOp>(op, result_type, permuted,
                                           output_shape);

    return success();
  }
};

class LowerBatchToSpaceND : public RewritePattern {

        quant_type = quant::DownCastScale(quant_type, min, max, op.getLoc());
      }
    }
    rewriter.setInsertionPointAfter(stats_op);
    Type result_type = quant_type.castFromExpressedType(stats_op.getType());
    auto q =
        rewriter.create<Q>(stats_op.getLoc(), result_type, stats_op.getArg());
    rewriter.replaceOpWithNewOp<DQ>(stats_op, stats_op.getType(), q);
    return success();
  }
};