dbName := DB.NamingStrategy.ColumnName("", name)
					resultType := reflect.ValueOf(first[dbName]).Type().Name()

					switch name {
					case "Name":
						if !strings.Contains(resultType, "string") {
							t.Errorf("invalid data type for %v, got %v %#v", dbName, resultType, first[dbName])
						}
					case "Age":
						if !strings.Contains(resultType, "int") {

    // Note that we are using the same `result_type` for both the
    // `TensorListStackOp` and `ConcatOp`, since the first dimension of the
    // shape specified by `result_type` is -1.
    auto stacked_extended_part = rewriter->create<TF::TensorListStackOp>(
        loc, result_type, extended_part,
        /*element_shape=*/CreateI32SplatConst(loc, rewriter, {}, -1),

      TensorType result_type = mlir::dyn_cast<TensorType>(result.getType());
      if (!result_type) {
        result_types.push_back(result.getType());
        continue;
      }
      QuantizedType qtype =
          mlir::dyn_cast<QuantizedType>(result_type.getElementType());
      if (!qtype) {
        result_types.push_back(result_type);
        continue;
      }

                            PatternRewriter &rewriter, Location loc,
                            Type result_type, Value input, Value filter,
                            Value bias) const {
    filter = legalizeFilter(rewriter, loc, filter);
    return rewriter.create<TFL::Conv2DOp>(
        loc, result_type, input, filter, bias,
        /*dilation_h_factor=*/state->dilation_height_factor,

    // 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 {

            output_scale_value, output_zero_point_value);

    auto new_dot_general_op = rewriter.create<stablehlo::DotGeneralOp>(
        op.getLoc(), /*resultType0=*/
        mlir::cast<TensorType>(op.getResult().getType())
            .clone(output_uniform_quantized_type),
        /*lhs=*/op.getLhs(), /*rhs=*/op.getRhs(),

mlir::TF::_XlaCompileMlirPlaceholderProgramKeyOp
CreateCompilationKeyPlaceholder(Location loc, OpBuilder& builder) {
  auto result_type =
      RankedTensorType::get({3}, builder.getType<mlir::TF::StringType>());
  return builder.create<mlir::TF::_XlaCompileMlirPlaceholderProgramKeyOp>(
      loc, /*program=*/result_type, llvm::ArrayRef<Value>{});
}

// Creates a `tf_device.launch` to wrap cluster ops.

class TestEventListenersAccessor;
class TestEventRepeater;
class UnitTestRecordPropertyTestHelper;
class WindowsDeathTest;
class UnitTestImpl* GetUnitTestImpl();
void ReportFailureInUnknownLocation(TestPartResult::Type result_type,
                                    const std::string& message);

}  // namespace internal

// The friend relationship of some of these classes is cyclic.

class TestEventListenersAccessor;
class TestEventRepeater;
class UnitTestRecordPropertyTestHelper;
class WindowsDeathTest;
class UnitTestImpl* GetUnitTestImpl();
void ReportFailureInUnknownLocation(TestPartResult::Type result_type,
                                    const std::string& message);

}  // namespace internal

// The friend relationship of some of these classes is cyclic.

  let hasRegionVerifier = 1;

  let builders = [
    OpBuilder<(ins "TypeRange":$resultTypes, "ValueRange":$operands,
      "llvm::ArrayRef<::mlir::NamedAttribute>":$attributes,
      "unsigned":$numRegions),
    [{
      assert(numRegions == 2u && "mismatched number of regions");
      build($_builder, $_state, resultTypes, operands, attributes);
    }]>];

  let hasCanonicalizer = 1;
}