// 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
  }];