{ tu64, tf32, 175 }, { tu64, tf64, 175 },

	{ tf32, tu8,  175 }, { tf32, ti16, 175 }, { tf32, tu16, 175 },
	{ tf32, ti32, 175 }, { tf32, tu32, 175 }, { tf32, ti64, 175 }, { tf32, tu64, 175 },
	{ tf32, tf32, 175 }, { tf32, tf64, 175 },

	{ tf64, tu8,  175 }, { tf64, ti16, 175 }, { tf64, tu16, 175 },
	{ tf64, ti32, 175 }, { tf64, tu32, 175 }, { tf64, ti64, 175 }, { tf64, tu64, 175 },
	{ tf64, tf32, 175 }, { tf64, tf64, 175 },

type TI int
type TI8 int8
type TI16 int16
type TI32 int32
type TI64 int64
type TU uint
type TU8 uint8
type TU16 uint16
type TU32 uint32
type TU64 uint64
type TUI uintptr
type TF float64
type TF32 float32
type TF64 float64
type TB bool
type TS string

func (v TI) String() string   { return Sprintf("I: %d", int(v)) }
func (v TI8) String() string  { return Sprintf("I8: %d", int8(v)) }

  int64_t axis = intAttr.getInt();
  if (axis < 0) {
    axis += rank;
  }
  return b->getI64IntegerAttr(axis);
}

// Returns a PrecisionConfig as an array attribute based on whether TF32
// execution is enabled
static ArrayAttr GetPrecisionConfig(Builder *builder) {
  mlir::mhlo::Precision precision = tsl::tensor_float_32_execution_enabled()
                                        ? mhlo::Precision::DEFAULT

    Additional details below.

*   Adds Support for
    [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/)
    on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for
    NVIDIA Ampere based GPUs and is enabled by default.

*   A major refactoring of the internals of the Keras Functional API has been