%cst = "tf.Const"()  {value = dense<1> : tensor<i1>}  : () -> tensor<i1>
    "tf.Yield"(%cst) : (tensor<i1>) -> ()
    }, {
  ^bb0(%barg0: tensor<2x?x!tf_type.qint8>, %barg1: tensor<?x2x!tf_type.qint8>):
    %id = "tf.Identity"(%barg0) : (tensor<2x?x!tf_type.qint8>) -> tensor<2x?x!tf_type.qint8>
    "tf.Yield"(%id, %barg1) : (tensor<2x?x!tf_type.qint8>, tensor<?x2x!tf_type.qint8>) -> ()

def TF_Qint8   : AnyTypeOf<
  [TF_TensorFlowType<"Qint8", "qint8">, TF_Qint8Ref],
  "8-bit quantized integer">;
def TF_Qint16  : AnyTypeOf<
  [TF_TensorFlowType<"Qint16", "qint16">, TF_Qint16Ref],
  "16-bit quantized integer">;
def TF_Qint32  : AnyTypeOf<
  [TF_TensorFlowType<"Qint32", "qint32">, TF_Qint32Ref],
  "32-bit quantized integer">;
def TF_Quint8  : AnyTypeOf<
  [TF_TensorFlowType<"Quint8", "quint8">, TF_Quint8Ref],

                                                             DataType type) {
  // Only qint8 and quint8 are considered here.
  double qmin, qmax;
  if (type == DT_QUINT8) {
    qmin = 0.0;
    qmax = 255.0;
  } else if (type == DT_QINT8) {
    qmin = -128.0;
    qmax = 127.0;
  } else {
    return absl::InvalidArgumentError("Only int8 and uint8 are considered.");
  }

      {"I64", "64-bit signless integer"},
      {"QI16", "QI16 type"},
      {"I8", "8-bit signless integer"},
      {"UI8", "8-bit unsigned integer"},
      {"QI8", "QI8 type"},
      {"QUI8", "QUI8 type"},
      {"TFL_Quint8", "TFLite quint8 type"},
  });

  return *entries;
}

void EmitDynamicRangeOp(std::vector<Record *> &defs, raw_ostream *ostream) {
  std::string dynamic_quant_kernel_support_regex =

      elemType.isInteger(64))
    return true;

  // Complex<F<32>> is allowed.
  if (mlir::isa<ComplexType>(elemType) &&
      mlir::cast<ComplexType>(elemType).getElementType().isF32())
    return true;

  // QUINT8 and UI8 are allowed.
  if (mlir::isa<TF::Quint8Type>(elemType) ||
      (elemType.isInteger(8) && mlir::cast<IntegerType>(elemType).isUnsigned()))
    return true;

  return false;
}

systems in general.

Operations in this dialect usually operate on tensor and scalar types defined in
the standard dialect. The extra defined types are specific to TensorFlow: `QINT`
types like !tf_type.qint8 (etc), `QUINT` types like !tf_type.quint8, all of the
`REF` types like !tf_type.uint8ref, as well as !tf_type.string,
!tf_type.resource, and !tf_type.variant which correspond to the tensorflow types
of the same name.

### Example:

    %check = "tf.Equal"(%arg1, %one) : (tensor<i32>, tensor<i32>) -> tensor<i1>
    "tf.Yield"(%check) : (tensor<i1>) -> ()
    }, {
  ^bb0(%barg0: tensor<2x?x!tf_type.qint8>, %barg1: tensor<?x2x!tf_type.qint8>):
    %id = "tf.Identity"(%barg0) : (tensor<2x?x!tf_type.qint8>) -> tensor<2x?x!tf_type.qint8>

		base.Fatalf("can only handle non-interfaces")
	}
	switch {
	case from.Size() == 2 && uint8(from.Alignment()) == 2:
		return "convT16", types.Types[types.TUINT16], false
	case from.Size() == 4 && uint8(from.Alignment()) == 4 && !from.HasPointers():
		return "convT32", types.Types[types.TUINT32], false
	case from.Size() == 8 && uint8(from.Alignment()) == uint8(types.Types[types.TUINT64].Alignment()) && !from.HasPointers():

	return t
}

// SetTypPtrs populates t.
func (t *Types) SetTypPtrs() {
	t.Bool = types.Types[types.TBOOL]
	t.Int8 = types.Types[types.TINT8]
	t.Int16 = types.Types[types.TINT16]
	t.Int32 = types.Types[types.TINT32]
	t.Int64 = types.Types[types.TINT64]
	t.UInt8 = types.Types[types.TUINT8]
	t.UInt16 = types.Types[types.TUINT16]
	t.UInt32 = types.Types[types.TUINT32]
	t.UInt64 = types.Types[types.TUINT64]

		{Name: "UINT", DBName: "fuint", BindNames: []string{"UINT"}, DataType: schema.Uint, Creatable: true, Updatable: true, Readable: true, Size: 64, Tag: `gorm:"column:fuint"`},
		{Name: "UINT8", DBName: "fuint8", BindNames: []string{"UINT8"}, DataType: schema.Uint, Creatable: true, Updatable: true, Readable: true, Size: 8, Tag: `gorm:"column:fuint8"`},