%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,
}

  // 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>

      offset_dims = [2, 3],
      collapsed_slice_dims = [0, 1],
      start_index_map = [0, 1],
      index_vector_dim = 2>,
    slice_sizes = array<i64: 1, 1, 2, 2>,
    indices_are_sorted = false
  } : (
    tensor<3x4x2x2x!quant.uniform<i8:f32, 3.000000e-01:-5>>,
    tensor<2x3x2xi64>
  ) -> tensor<2x3x2x2x!quant.uniform<i8:f32, 3.000000e-01:-5>>

  auto _indices_are_sorted = _o->indices_are_sorted;
  return tflite::CreateStablehloGatherOptions(
      _fbb,
      _offset_dims,
      _collapsed_slice_dims,
      _start_index_map,
      _index_vector_dim,
      _slice_sizes,
      _indices_are_sorted);
}

      scatter_dims_to_operand_dims_mlir.end());

  int64_t index_vector_dim = scatter_dimension_numbers.getIndexVectorDim();
  bool unique_indices = scatter_op.getUniqueIndices();
  bool indices_are_sorted = scatter_op.getIndicesAreSorted();

  auto update_window_dims = builder_.CreateVector(update_window_dims_vec);
  auto inserted_window_dims = builder_.CreateVector(inserted_window_dims_vec);

    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

        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<2x3x2xi32>) -> tensor<2x3x2x2xf32>
    return %0 : tensor<2x3x2x2xf32>
  }

  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<3x4x2xi32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32>
  func.return %2: tensor<2x3x2x2xi32>
}
// CHECK: %[[OPERAND:.*]] = stablehlo.constant
// CHECK: %[[INDICES:.*]] = stablehlo.constant