// CHECK-DAG: %[[zp:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
// CHECK: %0 = "tf.PartitionedCall"(%arg0, %[[q_w]], %[[scale]], %[[zp]]) <{config = "", config_proto = "", executor_type = "",
// CHECK-SAME: f = @quantized_matmul_fn_0}> : (tensor<2x12xf32>, tensor<12x2x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<*xf32>

    "tfl.yield"(%barg0, %cst) : (tensor<f32>, tensor<f32>) -> ()
  }) : (tensor<f32>, tensor<f32>) -> (tensor<f32>, tensor<f32>)
  func.return %0#1 : tensor<f32>
}

// -----

// Test case to test While op with resources that are not read-only variables.
// Do not remove resource arugments if they are not read-only variables to keep
// the graph's control dependency.
// CHECK-LABEL: WhileWithNonReadOnlyVariableResources

// CHECK: %[[dq0:.*]] = "quantfork.dcast"(%[[q0]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[maxpool:.*]] = "tf.MaxPool"(%[[dq0]])
// CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[reshape:.*]] = "tf.Reshape"(%[[dq1]]

    func.return %0 : tensor<?x19xf32>
  }
}

// CHECK-LABEL: HloModule main
// CHECK:       (arg_tuple.{{[0-9]+}}: (f32[10,17], f32[17,19])) -> (f32[10,19])

// NO_TUPLES-LABEL: HloModule main

  %0 = mhlo.constant dense<1.000000e+00> : tensor<f32>
  %1 = "mhlo.broadcast_in_dim"(%0) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>) -> tensor<6x6xf32>
  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %3 = "mhlo.reduce_window"(%arg0, %2) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
    %7 = mhlo.add %arg1, %arg2 : tensor<f32>
    mhlo.return %7 : tensor<f32>

  func.return %5: tensor<2x1x!quant.uniform<i8:f32, 0.003:-128>>
}

// CHECK:   func @quantizedOpOnly(%[[VAL_0:.*]]: tensor<1x!quant.uniform<i8:f32, 3.000000e-03:-128>>, %[[VAL_1:.*]]: tensor<1x!quant.uniform<i8:f32, 3.000000e-03:-128>>) -> tensor<2x1x!quant.uniform<i8:f32, 3.000000e-03:-128>> {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(%arg0: tensor<*x!quant.uniform<u16:f32, 2.0:37>>) -> tensor<*x!quant.uniform<u16:f32, 2.0:37>> {
// CHECK:     {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {
// CHECK-NEXT:      shape: [  ],
// CHECK-NEXT:      type: UINT16,
// CHECK-NEXT:      buffer: 1,

}

// CHECK-LABEL: @branch0
func.func @branch0(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Add" (%arg0, %arg1) {__op_key = 1, device = "/device:CPU:0"}  : (tensor<f32>, tensor<f32>) -> tensor<f32>
  func.return %0 : tensor<f32>
}

// CHECK-LABEL: @branch1
func.func @branch1(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {

!tf_res_f32 = tensor<*x!tf_type.resource<tensor<f32>>>
!tf_res_md_f32 = tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>> // Multi-dim f32

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  // CHECK-LABEL: func @main
  // CHECK-SAME: %[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<f32>>> {tf.device = "/device:TPU:0"},

  %q_w = "tfl.pseudo_qconst"() {qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<1024x1x1x1xi8>} : () -> tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
  %dq_w = "tfl.dequantize"(%q_w) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>