func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf64>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64> } : () -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8

  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf64>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64> } : () -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8

  EXPECT_THAT(num_bytes, Eq(16));
}

TEST_F(GetSizeInBytesTest, Float64ConstOpSizeInBytes) {
  constexpr absl::string_view kConstOpExpr =
      R"mlir(%cst = "tf.Const"() {value = dense<3.0> : tensor<2xf64>} : () -> tensor<2xf64>)mlir";

  Block block{};
  TF::ConstOp int_tensor_const_op = ParseConstOp(kConstOpExpr, block, ctx_);

  const int64_t num_bytes = GetSizeInBytes(int_tensor_const_op);
  EXPECT_THAT(num_bytes, Eq(16));

}

func.func @real(%arg0: tensor<1 x complex<f64>>) -> tensor<1xf64> {
  %0 = "tf.Real"(%arg0) : (tensor<1 x complex<f64>>) -> tensor<1xf64>
  func.return %0: tensor<1xf64>

// CHECK-LABEL: real
// CHECK:  "tfl.real"(%arg0) : (tensor<1xcomplex<f64>>) -> tensor<1xf64>
// CHECK:  return
}

func.func @imag(%arg0: tensor<1 x complex<f64>>) -> tensor<1xf64> {

      }
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_BOOL
    }
  }
}

# CHECK-DAG: tf.IteratorGetNext{{.+}}-> (tensor<1x8xbf16>, tensor<2x?x16xf32>, tensor<*xf64>)
# CHECK-DAG: tf.IteratorGetNextSync{{.+}}-> (tensor<*xi16>, tensor<3x24xi32>, tensor<?x4x32xi64>)
# CHECK-DAG: tf.MultiDeviceIteratorGetNextFromShard{{.+}}-> (tensor<5x40xf16>, tensor<*xcomplex<f32>>, tensor<6x48x?xcomplex<f64>>)

//CHECK-NEXT: return %0 : tensor<1x161x1xf32>
//CHECK-NEXT:}

func.func @convert(%arg0: tensor<2xf64>) -> tensor<2xf32> {
  %0 = "vhlo.convert_v1" (%arg0) : (tensor<2xf64>) -> tensor<2xf32>
  return %0 : tensor<2xf32>
}

//CHECK:func.func private @convert(%arg0: tensor<2xf64>) -> tensor<2xf32> {
//CHECK-NEXT: %0 = "vhlo.convert_v1"(%arg0) : (tensor<2xf64>) -> tensor<2xf32>
//CHECK-NEXT: return %0 : tensor<2xf32>
//CHECK-NEXT:}

  // CHECK-NEXT:   mhlo.return %[[CMP]]
  // CHECK-NEXT: }) : (tensor<8xf64>) -> tensor<8xf64>
  // CHECK-NEXT: return %[[SORT]]
  %output = "tf.XlaSort"(%input) : (tensor<8xf64>) -> (tensor<8xf64>)
  func.return %output : tensor<8xf64>
}

// -----

// CHECK-LABEL: @xlasort_const
func.func @xlasort_const() -> (tensor<2x3xi64>) {

      "mhlo.return"(%5) : (tensor<f32>) -> ()
    }) {
    base_dilations = dense<1> : tensor<5xi64>,
    padding = dense<0> : tensor<5x2xi64>,
    window_dilations = dense<1> : tensor<5xi64>,
    window_dimensions = dense<[1, 3, 3, 3, 1]> : tensor<5xi64>,
    window_strides = dense<[1, 2, 2, 2, 1]> : tensor<5xi64>} : (tensor<4x16x16x16x8xf32>, tensor<f32>) -> tensor<4x7x7x7x8xf32>
  %3 = mhlo.divide %2, %1 : tensor<4x7x7x7x8xf32>

    %cst = arith.constant dense<[2, 1, 200, 200, 200]> : tensor<5xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x200x200x200xf32>, tensor<1x200x200x200xf32>) -> tensor<2x200x200x200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<2x200x200x200xf32>, tensor<5xi64>) -> tensor<2x1x200x200x200xf32>
    func.return %1 : tensor<2x1x200x200x200xf32>
  }

// CHECK:  return %[[CST]]
}

// CHECK-LABEL: @cast_i64_to_i32
func.func @cast_i64_to_i32() -> tensor<5xi32> {
  %cst = arith.constant dense<[-1, 0, 1, 2147483647, 2147483648]> : tensor<5xi64>
  %0 = "tfl.cast"(%cst) : (tensor<5xi64>) -> tensor<5xi32>
  func.return %0 : tensor<5xi32>

// CHECK: %[[CST:.*]] = arith.constant dense<[-1, 0, 1, 2147483647, -2147483648]> : tensor<5xi32>
// CHECK:  return %[[CST]]
}