// CHECK:           %[[VAL_2:.*]] = stablehlo.tuple %[[VAL_1]] : tuple<tensor<1x2xf32>>
// CHECK:           return %[[VAL_2]] : tuple<tensor<1x2xf32>>
// CHECK:         }
func.func @tfl_wrapped_jax_random_uniform(%arg0: tensor<2xui32>) -> tuple<tensor<1x2xf32>> {
  // This is a fake jax random uniform body.

}

func.func @QDQsFollowedByTranspose(tensor<1x2xf32>) -> (tensor<2x1xf32>) {
^bb0(%arg0: tensor<1x2xf32>):
  %cst_0 = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0){qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>}: (tensor<1x2xf32>) -> (tensor<1x2x!quant.uniform<u8:f32, 1.0>>)
  %1 = "tfl.dequantize"(%0): (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1x2xf32>)

  %add = stablehlo.add %dq1, %dq2 : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
  func.return %add : tensor<3x2xf32>
}

// -----

// CHECK-LABEL: @quantize_constant
// CHECK-SAME: %[[ARG0:.*]]: tensor<1x3xf32>
func.func @quantize_constant(%arg0: tensor<1x3xf32>) -> tensor<1x2xf32> {

          %outputs_5, %control_6 = tf_executor.island(%control_4) wraps "tf._XlaHostComputeMlir"() {host_mlir_module = "module {\0A func.func @host_func() -> tensor<1x2xf32> {\0A %0 = \22tf.Const\22() {value = dense<0.1> : tensor<1x2xf32>} : () -> tensor<1x2xf32> \0A return %0 : tensor<1x2xf32>}}", manual_sharding = true, recv_key = "host_compute_channel_1_retvals", send_key = "host_c...

func.func @fakeQuantFollowedByReshape(tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> (tensor<2x1xf32>) {
^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>):
  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>

    return %2 : tensor<1x3xf32>
  }

  func.func private @composite_dot_general_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    return %0 : tensor<1x3xf32>
  }
}

func.func @fakeQuantFollowedByReshape(tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> (tensor<2x1xf32>) {
^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>):
  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>

      return %1 : tensor<1x3xf32>
    }

    func.func private @composite_dot_general_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> {
      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
      return %0 : tensor<1x3xf32>
    }
  )mlir";

  %0 = arith.constant dense<[[1.0, 2.0]]> : tensor<1x2xf32>
  %1 = "tfl.batch_matmul"(%arg0, %0) {adj_x = false, adj_y = true, asymmetric_quantize_inputs = false} : (tensor<4x128x2xf32>, tensor<1x2xf32>) -> tensor<4x128x1xf32>
  func.return %1 : tensor<4x128x1xf32>
  // CHECK: %[[CONST_WEIGHT:.*]] = arith.constant
  // CHECK-SAME: [1.000000e+00, 2.000000e+00]
  // CHECK-SAME: tensor<1x2xf32>

    : ( tensor<1x28x28xf32>,
        tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>,
        tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>,
        none, none, none,
        tensor<20xf32>, tensor<20xf32>, tensor<20xf32>, tensor<20xf32>,
        none, none,
        tensor<1x20xf32>, tensor<1x20xf32>,
        none, none, none, none) -> tensor<1x28x20xf32>