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>>

      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);

    %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>
  }

  // CHECK-LABEL: func @xla_gather_with_fold