def : Pat<(TF_XlaReplicaIdOp),
          (TF_CastOp (MHLO_ReplicaIdOp), /*truncate=*/ConstBoolAttrFalse)>;

//===----------------------------------------------------------------------===//
// XlaGather op.
//===----------------------------------------------------------------------===//

def ToGatherDimNumsAttr : NativeCodeCall<"GetGatherDimNumsAttr($0, &$_builder)">;

                                         "XlaDynamicSlice",
                                         "XlaDynamicUpdateSlice",
                                         "XlaEinsum",
                                         "XlaGather",
                                         "XlaIf",
                                         "XlaKeyValueSort",
                                         "XlaPad",
                                         "XlaRecv",

  %0 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
  %1 = "tf.Const"() {value = dense<[1, 9, 23, 768]> : tensor<4xi32>} : () -> tensor<4xi32>
  %2 = "tf.XlaGather"(%arg0, %0, %1) {device = "", dimension_numbers = "\0A\04\00\01\02\03\1A\01\02", indices_are_sorted = false} : (tensor<1x9x104x768xf32>, tensor<1xi32>, tensor<4xi32>) -> tensor<?x?x?x?xf32>
  func.return %2 : tensor<?x?x?x?xf32>

  func.func @xla_gather(%arg0: tensor<1x1x9xi32>, %arg1: tensor<1xi32>) -> tensor<*xi32> {
    %cst = "tf.Const"() {value = dense<[1, 1, 8]> : tensor<3xi32>} : () -> tensor<3xi32>
    %0 = "tf.XlaGather"(%arg0, %arg1, %cst) {dimension_numbers = "\0A\03\00\01\02\1A\01\02", indices_are_sorted = true} : (tensor<1x1x9xi32>, tensor<1xi32>, tensor<3xi32>) -> tensor<*xi32>
    func.return %0 : tensor<*xi32>
  }

      "XlaCustomCall",
      "XlaCustomCallV2",
      "XlaDequantize",
      "XlaDot",
      "XlaDotV2",
      "XlaDynamicSlice",
      "XlaDynamicUpdateSlice",
      "XlaEinsum",
      "XlaGather",
      "XlaIf",
      "XlaKeyValueSort",
      "XlaOptimizationBarrier",
      "XlaPad",
      "XlaRecv",
      "XlaReduce",
      "XlaReducePrecision",
      "XlaReduceScatter",

  // CHECK-SAME:   start_index_map = [0, 1]
  // CHECK-SAME:   index_vector_dim = 1
  // CHECK-SAME: indices_are_sorted = true
  // CHECK-SAME: slice_sizes = dense<[1, 1, 300]> : tensor<3xi64>

  %0 = "tf.XlaGather"(%arg0, %arg1, %cst) {dimension_numbers = "\0A\02\00\01\12\01\00\1A\02\00\01\20\01", indices_are_sorted = true} : (tensor<200x100x300xf32>, tensor<10x2xi32>, tensor<3xi64>) -> tensor<1x300x10xf32>
  func.return %0 : tensor<1x300x10xf32>

  }

  return GetI64ElementsAttr(normalized_sizes, builder);
}

//===----------------------------------------------------------------------===//
// XlaGather op utilities.
//===----------------------------------------------------------------------===//

bool HasValidGatherDims(StringAttr attr) {
  ::xla::GatherDimensionNumbers dims;

  let results = (outs
    TensorOf<[TF_Bfloat16, TF_Complex64, TF_Float32]>:$product
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_XlaGatherOp : TF_Op<"XlaGather", [Pure, TF_NoConstantFold]> {
  let summary = "Wraps the XLA Gather operator documented at";

  let description = [{
https://www.tensorflow.org/xla/operation_semantics#gather
  }];