%0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

  %0 = "tf.Pad"(%arg0, %padding) : (tensor<3xf32>, tensor<1x2xi64>) -> tensor<6xf32>
  func.return %0 : tensor<6xf32>
}

// CHECK-LABEL: func @pad_bf16
func.func @pad_bf16(%arg0: tensor<3xbf16>) -> tensor<6xbf16> {
  %padding = "tf.Const"() { value = dense<[[1, 2]]> : tensor<1x2xi64> } : () -> tensor<1x2xi64>
  // CHECK-DAG: [[PAD:%.+]] = "tf.Const"() {{.+}}  -> tensor<1x2xi64>

  %1 = "quantfork.stats"(%0) {
    layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>,
    axisStats = dense<[
      [-1.0, 1.0],
      [-8.0, 8.0],
      [-0.5, 0.5]
    ]> : tensor<3x2xf32>, axis = 2 : i64
  } : (tensor<8x4x3xf32>) -> tensor<8x4x3xf32>
  func.return %1 : tensor<8x4x3xf32>