func.return %0 : tensor<1x2xi1>
}

// CHECK-LABEL:   func @less_equal(
// CHECK-SAME:                     %[[VAL_0:.*]]: tensor<2xi32>,
// CHECK-SAME:                     %[[VAL_1:.*]]: tensor<2xi32>) -> tensor<2xi1> {
// CHECK:           %[[VAL_2:.*]] = "tf.LessEqual"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi1>
// CHECK:           return %[[VAL_2]] : tensor<2xi1>

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_LessEqualOp : TF_Op<"LessEqual", [Pure, ResultsBroadcastableShape]>,
                     WithBroadcastableCmpOpBuilder {
  let summary = "Returns the truth value of (x <= y) element-wise.";

  let description = [{
*NOTE*: `LessEqual` supports broadcasting. More about broadcasting
[here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

			},
		},
	},
	{
		name:   "LessEqual",
		argLen: 1,
		reg: regInfo{
			outputs: []outputInfo{
				{0, 1073733624}, // R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29
			},
		},
	},
	{
		name:   "FLessEqual",
		argLen: 1,
		reg: regInfo{
			outputs: []outputInfo{

  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
  CAST = 53,
  PRELU = 54,
  MAXIMUM = 55,
  ARG_MAX = 56,
  MINIMUM = 57,
  LESS = 58,
  NEG = 59,
  PADV2 = 60,
  GREATER = 61,
  GREATER_EQUAL = 62,
  LESS_EQUAL = 63,
  SELECT = 64,
  SLICE = 65,
  SIN = 66,
  TRANSPOSE_CONV = 67,
  SPARSE_TO_DENSE = 68,
  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,

  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
  CAST = 53,
  PRELU = 54,
  MAXIMUM = 55,
  ARG_MAX = 56,
  MINIMUM = 57,
  LESS = 58,
  NEG = 59,
  PADV2 = 60,
  GREATER = 61,
  GREATER_EQUAL = 62,
  LESS_EQUAL = 63,
  SELECT = 64,
  SLICE = 65,
  SIN = 66,
  TRANSPOSE_CONV = 67,
  SPARSE_TO_DENSE = 68,
  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,

def TFL_LessEqualOp : TFL_Op<"less_equal", [
    ResultsBroadcastableShape,
    QuantizableResult,
    ComparisonOpSameElementTypeConstraint,
    TFL_OperandsHaveSameShapesOrBroadcastableShape<[0, 1], 4>,
    Pure]> {
  let summary = "Less_equal operator";

  let description = [{
    Element-wise less_equal operation.
  }];

  let arguments = (

    *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
    *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.

* `tf.lite`
    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication

### Bug Fixes and Other Changes

    "CAST",
    "PRELU",
    "MAXIMUM",
    "ARG_MAX",
    "MINIMUM",
    "LESS",
    "NEG",
    "PADV2",
    "GREATER",
    "GREATER_EQUAL",
    "LESS_EQUAL",
    "SELECT",
    "SLICE",
    "SIN",
    "TRANSPOSE_CONV",
    "SPARSE_TO_DENSE",
    "TILE",
    "EXPAND_DIMS",
    "EQUAL",
    "NOT_EQUAL",
    "LOG",
    "SUM",