op->unique_indices)));
    attributes.emplace_back(builder.getNamedAttr(
        "indices_are_sorted",
        mlir::vhlo::BooleanV1Attr::get(builder.getContext(),
                                       op->indices_are_sorted)));
    attributes.emplace_back(builder.getNamedAttr(
        "index_vector_dim", BuildVhloIntV1Attr(op->index_vector_dim, builder)));
    return;
  }

    start_index_map = #vhlo.tensor_v1<dense<[1, 0]> : tensor<2xi64>>,
    index_vector_dim = #vhlo.integer_v1<2 : i64>,
    slice_sizes = #vhlo.tensor_v1<dense<[1, 2, 2]> : tensor<3xi64>>,
    indices_are_sorted = #vhlo.bool_v1<false>
  }> : (tensor<3x4x2xi32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32>
  return %result : tensor<2x3x2x2xi32>
}

                          $dimension_numbers, $indices_are_sorted),
          (MHLO_GatherOp $operand, $start_indices,
                        (ToGatherDimNumsAttr $dimension_numbers),
                        (CastElementsToI64Elements $slice_sizes),
                        $indices_are_sorted),
          [(HasValidGatherDims $dimension_numbers)]>;

            offset_dims = [2, 3],
            collapsed_slice_dims = [0],
            start_index_map = [1, 0],
            index_vector_dim = 2>,
          slice_sizes = array<i64: 1, 2, 2>,
          indices_are_sorted = false
        } : (tensor<3x4x2xf32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xf32>
        func.return %2 : tensor<2x3x2x2xf32>
      }
    }
  )mlir";

    %cst = "tf.Const"() { value = dense<[1, 1, 300]> : tensor<3xi64> } : () -> tensor<3xi64>
    %0 = "tf.XlaGather"(%cst_0, %arg0, %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>
    %1 = "tf.Identity"(%0) {device = ""} : (tensor<1x300x10xf32>) -> tensor<1x300x10xf32>
    func.return %1 : tensor<1x300x10xf32>

}

table StablehloGatherOptions{
  offset_dims : [long];
  collapsed_slice_dims : [long];
  start_index_map : [long];
  index_vector_dim : long;
  slice_sizes : [long];
  indices_are_sorted : bool;
}

table StablehloTransposeOptions{
  permutation : [long];
}

enum StablehloPrecisionConfig : uint {
  DEFAULT,
  HIGH,
  HIGHEST,
}

    dimension_numbers = #stablehlo.gather<
      offset_dims = [2, 3],
      collapsed_slice_dims = [0],
      start_index_map = [1, 0],
      index_vector_dim = 2>,
    slice_sizes = array<i64: 1, 2, 2>,
    indices_are_sorted = false
  } : (tensor<3x4x2xf32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xf32>
    %8 = "quantfork.qcast"(%7) {volatile} : (tensor<2x3x2x2xf32>) -> tensor<2x3x2x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>

  // CHECK:      %[[RESULT:.*]] = "mhlo.scatter"(%[[DEFAULT]], %[[ARG0]], %[[ARG1]])
  // CHECK-SAME: indices_are_sorted = false
  // CHECK-SAME: scatter_dimension_numbers
  // CHECK-SAME:   inserted_window_dims = [0, 1]
  // CHECK-SAME:   scatter_dims_to_operand_dims = [0, 1]
  // CHECK-SAME:   index_vector_dim = 1