"tf.Assert"(%0, %arg1) {summarize = 3} : (tensor<1xi1>, tensor<1xi32>) -> ()
  func.return %0 : tensor<1xi1>
  // CHECK-LABEL: preserve_assert
  // CHECK: tfl.less_equal
  // CHECK: Assert
  // CHECK: return

  // CHECK-NEXT:  mhlo.compare LT, %arg0, %arg1
  %0 = "tf.Less"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi1>
  func.return %0: tensor<2xi1>
}

// CHECK-LABEL: func @less_equal
func.func @less_equal(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2xi1> {
  // CHECK-NEXT:  mhlo.compare LE, %arg0, %arg1
  %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi1>

  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,

// we can't catch this error.
func.func @less_equal(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> {
  %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1>
  func.return %0 : tensor<8x16xi1>

// CHECK-LABEL: less_equal
// CHECK:  tfl.less_equal(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1>
// CHECK:  return
}

  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 = (

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

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

[here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

Example:

```python
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less_equal(x, y) ==> [True, True, False]

x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 6])
tf.math.less_equal(x, y) ==> [True, True, True]
```
  }];

  let arguments = (ins
    TF_IntOrFpTensor:$x,
    TF_IntOrFpTensor:$y
  );

        `tf.atan2`, `tf.atanh`, `tf.cos`, `tf.cosh`, `tf.equal`, `tf.exp`,
        `tf.floor`, `tf.greater`, `tf.greater_equal`, `tf.less`,
        `tf.less_equal`, `tf.log`, `tf.logp1`, `tf.logical_and`,
        `tf.logical_not`, `tf.logical_or`, `tf.maximum`, `tf.minimum`,
        `tf.not_equal`, `tf.sin`, `tf.sinh`, `tf.tan`
    *   Deprecate `tf.data.Dataset.shard`.