m() [unsafe.Sizeof(x3.m)]int
}

var x3 T3

type T4 /* ERROR "invalid recursive type" */ interface {
	m() [unsafe.Sizeof(cast4(x4.m))]int // cast is invalid but we have a cycle, so all bets are off
}

var x4 T4
var _ = cast4(x4.m)

*   `tf.nn.embedding_lookup_sparse`

    *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`

                } catch (Throwable e) {
                    LoggerFactory.getLogger(getClass()).warn("Unable to load JUnit4 runner to calculate Ignored test cases", e);
                }
            }
            return null;
        }
    }

    private static class IgnoredIgnoredBuilder extends IgnoredBuilder {
        @Override
        public Runner runnerForClass(Class<?> testClass) {

			}
			fd := fncData{o.name, s.name, o.symbol, "", ""}

			// unsigned test cases
			if len(s.u) > 0 {
				for _, i := range s.u {
					fd.Number = fmt.Sprintf("%d", i)
					fd.FNumber = strings.Replace(fd.Number, "-", "Neg", -1)

					// avoid division by zero
					if o.name != "mod" && o.name != "div" || i != 0 {
						// introduce uint64 cast for rhs shift operands
						// if they are too large for default uint type

// If there is a change in supporting more types in the TFLite cast op kernel,
// the While loop outline pass should be updated since it inserts cast op(s)
// after the TF -> TFL legalization pass is done.
// LINT.IfChange
def TFL_CastOp : TFL_Op<"cast", [
    Pure,
    SameOperandsAndResultShape]> {
  let summary = "Cast operator";

  let description = [{
    Casts input from input type to output type.
  }];

    %float_in_zp = "tf.Cast"(%input_zp) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32>
    %float_out_zp = "tf.Cast"(%out_zp) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32>

    %cast = "tf.Cast"(%input) {Truncate = false} : (tensor<*xi8>) -> tensor<*xf32>
    %sub = "tf.Sub"(%cast, %float_in_zp) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

llvm::StringRef ProcessIndexPath(mlir::ArrayAttr index_path) {
  if (index_path.size() == 1 && mlir::isa<mlir::StringAttr>(index_path[0])) {
    // TODO(chky): Support cases where index_path is not a single string.
    return mlir::cast<mlir::StringAttr>(index_path[0]).getValue();
  }
  return "";
}

absl::StatusOr<std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
ProcessTensorSpec(mlir::TensorType type) {

#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/tfrt/fallback/cost_recorder.h"
#include "tensorflow/core/tfrt/fallback/op_cost_map.pb.h"

namespace tensorflow {
namespace tfrt_compiler {

// Analyze costs for tensorflow operations.
//
// The current heuristic used is quite simple, which is to calculate the total
// size of input tensors. The exception is that ops whose cost is irrelevant to

        // 1) Both attributes are strongly typed and match, usually the case if both are sourced from the local build
        // 2) Both attributes are desugared, usually the case if both are sourced from published metadata
        if (otherAttributeType.isAssignableFrom(attribute.getType())) {
            return Cast.uncheckedCast(get());
        }

TypeSafeDiagnosingMa<T> extends BaseMatcher<T> Convenient base class for Matchers that require a non-null value of a specific type and that will report why the received value has been rejected. This implements the null check, checks the type and then casts. To use, implement matchesSafely() . Author: Neil Dunn, Nat Pryce, Steve Freeman Constructor Summary protected TypeSafeDiagnosingMa() The default constructor for simple sub types protected TypeSafeDiagnosingMa(java.lang.Class<?> expectedType) Use this...