// CHECK-SAME: tfl.schema_version = 3 : i32

  // CHECK:          %{{.*}} = "tfl.pseudo_const"() <{value = dense<{{\[\[1, 2\], \[3, 4\], \[5, 6\]\]}}> : tensor<3x2xi32>}>
  // CHECK-NEXT:     [[SUB:%.*]] = tfl.sub %{{.*}}, %{{.*}} {fused_activation_function = "RELU6"} : tensor<3x2xi32>
  // CHECK-NEXT:     [[SCALAR:%.*]] = "tfl.pseudo_const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>

  %const = "tfl.pseudo_const"() {value = dense<[1.0]> : tensor<1xf32>} : () -> tensor<1xf32>
  %out = "tfl.add"(%const, %arg) { fused_activation_function = "NONE" } : (tensor<1xf32>, tensor<!tf_type.resource<tensor<1xf32>>>) -> (tensor<1xf32>)
  func.return %out : tensor<1xf32>
}
// CHECK-NEXT: %[[CONST:.*]] = "tfl.pseudo_const"()

// CHECK:             quantization: {
// CHECK:             }
// CHECK:           }, {
// CHECK:             shape: [ 3, 2 ],
// CHECK:             buffer: 2,
// CHECK:             name: "tfl.pseudo_const",
// CHECK:             quantization: {
// CHECK:             },
// CHECK:             has_rank: true
// CHECK:           }, {
// CHECK:             shape: [ 3, 2 ],
// CHECK:             buffer: 3,

  %0 = "tfl.pseudo_const"() {value = dense<["image/encoded", "image/text"]> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>
  %1 = "tfl.pseudo_const"() {value = dense<"image/encoded"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
  %2 = "tfl.pseudo_const"() {value = dense<"image/text"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>

// Confirm output-arrays works.

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.squared_difference"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("squared_difference")

  // CHECK: }

  // Test that i4 buffers are densely packed, i.e. [-8, 3, -2, -5] should be
  // be packed low-bits-first as [0x38, 0xBE] or [56, 190]. Tensor type should
  // be INT4.
  %0 = "tfl.pseudo_const" () {value = dense<[-8, 3, -2, -5]> : tensor<4xi4>} : () -> tensor<4xi4> loc("Const")
  func.return %0 : tensor<4xi4>

  // CHECK-NEXT:   buffer: 6
  // CHECK-NEXT:   } ]
  // CHECK-NEXT:   signature_defs: [ ]
  // CHECK-NEXT: }
  // CHECK-EMPTY:

  %0 = "tfl.pseudo_const" () {value = dense<true> : tensor<4xi1>} : () -> tensor<4xi1> loc("Const1")
  %1 = "tfl.pseudo_const" () {value = dense<false> : tensor<4xi1>} : () -> tensor<4xi1> loc("Const2")
  %2 = "tfl.logical_or"(%arg0, %1) : (tensor<4xi1>, tensor<4xi1>) -> tensor<4xi1> loc("logical_or")

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [

// CHECK: tf.MyCustomOp(tensor<4xf32>, tensor<4xf32>) -> (tensor<4xf32>, tensor<3xf32>) : {name = "MyCustomOp"}

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
  %0 = "tfl.pseudo_const" () {name = "Const", value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32>
  %1 = "tfl.mul"(%arg0, %0) {fused_activation_function = "NONE", name = "mul"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>

// TODO(b/329300758): re-enable filecheck | FileCheck %s
// Tests -input-arrays flag.

func.func @main(%arg0: tensor<4xf32>) -> tensor<4xf32> {
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.squared_difference"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("squared_difference")