func.return %0 : tensor<?x?xf32>
}

// -----

func.func @testOneHot(%indices: tensor<3xi32>, %depth: tensor<i32>, %on_value: tensor<f32>, %off_value: tensor<f32>) -> tensor<3x5xf32> {
  %result = "tf.OneHot"(%indices, %depth, %on_value, %off_value) {axis = -1 : i64} : (tensor<3xi32>, tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<3x5xf32>
  func.return %result : tensor<3x5xf32>
}

// -----

  // CHECK: return %0 : tensor<2x3x4x5xf32>
}

func.func @OneHot(%arg0: tensor<3xi32>, %arg1: tensor<i32>, %arg2: tensor<f32>, %arg3: tensor<f32>) -> tensor<*xf32> {
  %0 = "tf.OneHot"(%arg0, %arg1, %arg2, %arg3) {axis = -1 : i64} : (tensor<3xi32>, tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<*xf32>
  func.return %0: tensor<*xf32>

// CHECK-LABEL: OneHot

  return true;
}

// Returns true if attr is a DenseIntElementsAttr of int32 or int64 values or an
// incrementing sequence from 0 to N-1.
//
// If such a value is used in an Equal operator, it can be replaced with OneHot.
bool IsOneHotIndexAttribute(Attribute attr) {
  const auto dense_attr = mlir::dyn_cast_or_null<DenseIntElementsAttr>(attr);
  if (!dense_attr) {
    return false;
  }
  auto index_type = dense_attr.getType();

  let hasOptions = 1;
  let customOption = "ReducerOptions";
}

def TFL_OneHotOp : TFL_Op<"one_hot", [
    QuantizableResult,
    Pure]> {
  let summary = "OneHot operator";

  let description = [{
    Returns a one-hot tensor.The locations represented by indices in `indices`
    take value `on_value`, while all other locations take value `off_value`.

	"⇗":                           "\u21d7",
	"⤤":                          "\u2924",
	"↗":                           "\u2197",
	"↗":                         "\u2197",
	"≐̸":                           "\u2250\u0338",
	"≢":                          "\u2262",
	"⤨":                          "\u2928",
	"≂̸":                           "\u2242\u0338",