// CHECK: %[[v1:.*]] = "tf.BatchMatMulV2"(%[[v0]], %arg1) <{adj_x = false, adj_y = false}> : (tensor<2x5x1x7xf32>, tensor<2x5x7x3xf32>) -> tensor<2x5x1x3xf32>
  // CHECK: %[[v2:.*]] = "tf.Reshape"(%[[v1]], %[[cst_1]]) : (tensor<2x5x1x3xf32>, tensor<3xi64>) -> tensor<2x5x3xf32>
  // CHECK: return %[[v2]] : tensor<2x5x3xf32>
}

    %12 = "quantfork.qcast"(%11) {volatile} : (tensor<2x3x1x3xf32>) -> tensor<2x3x1x3x!quant.uniform<i8:f32, 3.000000e-01:1>>
    %13 = "quantfork.dcast"(%12) : (tensor<2x3x1x3x!quant.uniform<i8:f32, 3.000000e-01:1>>) -> tensor<2x3x1x3xf32>
    return %13 : tensor<2x3x1x3xf32>
  }

  // CHECK: quantized_dot_general_fn_1

  %conv2 = "tfl.conv_2d"(%0, %w, %b2) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",
    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
  } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  func.return %conv, %conv2 : tensor<1x5x5x3xf32>, tensor<1x5x5x3xf32>

func.func @CheckNumericVerifyMultipleUsers(%arg0: tensor<1x5x5x3xf32>) -> tensor<1x5x5x3xf32> {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>, volatile} : (tensor<1x5x5x3xf32>) -> tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>
  %1 = "tfl.dequantize"(%0) : (tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>) -> tensor<1x5x5x3xf32>

  %cst = "tf.Const"() {value = dense<1.000000e+00> : tensor<2x3x3x3xf32>} : () -> tensor<2x3x3x3xf32>
  %cst_0 = "tf.Const"() {value = dense<0.500000e+00> : tensor<1x3x2x3xf32>} : () -> tensor<1x3x2x3xf32>
  %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x3xf32>) -> tensor<1x3x2x3xf32>

    func.return %result : tensor<10x24x24x64xf32>
  }

  // CHECK-LABEL: @max_pool_grad_same
  func.func @max_pool_grad_same(%orig_input: tensor<2x13x25x7xf32>, %orig_output: tensor<2x4x7x7xf32>, %grad: tensor<2x4x7x7xf32>) -> tensor<2x13x25x7xf32> {
    // CHECK: padding = dense<{{\[\[}}0, 0], [0, 1], [1, 1], [0, 0]]> : tensor<4x2xi64>
    %result = "tf.MaxPoolGrad"(%orig_input, %orig_output, %grad) {
      data_format = "NHWC",