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

  %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>

// CHECK-DAG: %[[CST:.*]] = arith.constant dense<0> : tensor<4xi64>