}

// -----

func.func @testBatchMatmulHybridQuant(%arg0 : tensor<1x4x384x32xf32>, %arg1 : tensor<1x4x384x32x!quant.uniform<i8:f32, 0.11:-16>>) -> tensor<1x4x384x384xf32> {
  // CHECK: "tfl.batch_matmul"(%arg0, %arg1)
  %0 = "tfl.batch_matmul"(%arg0, %arg1) {adj_x = false, adj_y = true} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32x!quant.uniform<i8:f32, 0.11:-16>>) -> tensor<1x4x384x384xf32>

  // CHECK: %[[CST_0:.*]] = "tfl.no_value"() <{value}> : () -> none
  // CHECK: %[[ARG1:.*]] = "tfl.transpose_conv"(%arg0, %[[ARG0]], %arg2, %[[CST_0]]) <{fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}> : (tensor<4xi32>, tensor<1x3x3x32xf32>, tensor<15x14x14x32xf32>, none) -> tensor<15x28x28x1xf32>

  func.func @operand_as_shape(%18: tensor<i32>, %39: tensor<1x4x4x32xf32>) -> () {
    %cst_5 = arith.constant dense<512> : tensor<i32>
    %19 = "tf.Pack"(%18, %cst_5) {N = 2 : i64, T = i32, axis = 0 : i64, device = ""} : (tensor<i32>, tensor<i32>) -> tensor<2xi32>
    // CHECK: -> tensor<1x512xf32>
    %40 = "tf.Reshape"(%39, %19) {T = f32, Tshape = i32, device = ""} : (tensor<1x4x4x32xf32>, tensor<2xi32>) -> tensor<?x?xf32>
   func.return
  }