// CHECK: %[[ADD:.*]] = tfl.add %[[VAR_VAL]], %arg0 {fused_activation_function = "NONE"} : tensor<1x10xf32>
  // CHECK: "tfl.assign_variable"(%[[RESOURCE]], %[[ADD]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()
  // CHECK: %[[RESULT:.*]] = "tfl.read_variable"(%[[RESOURCE]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>) -> tensor<1x10xf32>

}

// -----

func.func @testBatchMatMulV2(%lhs: tensor<f32>, %rhs: tensor<10x10xf32>) {
  // expected-error @+1 {{requires lhs operand to have rank at least two}}
  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<f32>, tensor<10x10xf32>) -> tensor<10x10xf32>
}

// -----

func.func @testBatchMatMulV2(%lhs: tensor<10x10xf32>, %rhs: tensor<f32>) {
  // expected-error @+1 {{requires rhs operand to have rank at least two}}

// CHECK-LABEL: float_matmul
func.func @float_matmul(
  %arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

// CHECK: return %[[GATHER]] : tensor<1x300x10xf32>

// -----

module {
  func.func @propagate_xlagather(%arg0: tensor<10x2xi32>) -> tensor<1x300x10xf32> {
    %cst = "tf.Const"() {value = dense<[1, 1, 300]> : tensor<3xi64>} : () -> tensor<3xi64>

    func.return %2 : tensor<1xi32>
  }

  // CHECK-LABEL: func @tensor_cast_partial_infer
  func.func @tensor_cast_partial_infer(%arg0: tensor<?x10xi32>) -> tensor<10x?xi32> {
   // CHECK: return {{.*}} : tensor<10x10xi32>
    %2 = tensor.cast %arg0 : tensor<?x10xi32> to tensor<10x?xi32>
    func.return %2 : tensor<10x?xi32>
  }

  // CHECK-LABEL: operand_pack_unranked

  "tfl.custom"(%0, %2) {custom_code = "FlexAssignVariableOp", custom_option = #tfl<const_bytes : "0x1041737369676E5661726961626C654F70003B121041737369676E5661726961626C654F701A001A002A0B0A056474797065120230012A140A0E76616C69646174655F736861706512022800320000024F3E1414042801">} : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()

    %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false

  // CHECK-NEXT: %[[UPDATE:.*]] = "tf.XlaDynamicUpdateSlice"(%[[STACK_VAL]], %[[UPDATE_SLICE]], %[[CONCAT_OFFETS]]) : (tensor<10x2xi32>, tensor<1x2xi32>, tensor<2xi32>) -> tensor<10x2xi32>
  // CHECK-NEXT: "tf.AssignVariableOp"(%[[BUFFER]], %[[UPDATE]]) : (tensor<!tf_type.resource<tensor<10x2xi32>>>, tensor<10x2xi32>) -> ()
  // CHECK-NEXT: %[[CONST1:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>

// -----

func.func @constant_tensor_invalid_3(%arg0: vector<1xi32>) -> tensor<1x1xi32> {
    // expected-error@+1 {{input and output should have same shape and element type}}
  %0 = "tfr.constant_tensor"(%arg0) : (vector<1xi32>) -> tensor<1x1xi32>
  func.return %0 : tensor<1x1xi32>
}

// -----

func.func @constant_tensor_invalid_4(%arg0: i32) -> tensor<1x1xi32> {
    // expected-error@+1 {{input can not be converted to an output tensor}}

}

func.func @matmul_batch(%arg0: tensor<10x15xf32>, %arg1: tensor<15x17xf32>) -> tensor<10x17xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", device = "/device:CPU:0", name = "MatMul", adj_x = false, adj_y = false} :
(tensor<10x15xf32>, tensor<15x17xf32>) -> tensor<10x17xf32>
  func.return %0 : tensor<10x17xf32>
// CHECK-LABEL: matmul_batch