%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:"tf.MlirPassthroughOp"
# CHECK: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"

// CHECK: return %[[RESULT0]]
}

// CHECK-LABEL: testMatmulAsymAttributeTrue
func.func @testMatmulAsymAttributeTrue(%arg0: tensor<4x10x15xf32>, %arg1: tensor<4x15x17xf32>) -> tensor<4x10x17xf32> {
  %0 = "tfl.batch_matmul"(%arg0, %arg1) {adj_x = false, adj_y = false, asymmetric_quantize_inputs = true} : (tensor<4x10x15xf32>, tensor<4x15x17xf32>) -> tensor<4x10x17xf32>

  // 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>

  %2 = "tfl.pseudo_const"() {value = dense<[[0.2]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %3 = "tfl.pseudo_const"() {value = dense<[[0.3]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %4 = "tfl.pseudo_const"() {value = dense<[[0.4]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %5 = "tfl.pseudo_const"() {value = dense<[[0.5]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>

    return %result : tensor<1x1xf32>
  }
}

// -----
// Variable tensor for host can still be used.
//
// CHECK-LABEL:  func.func @serving_default(%arg0: tensor<1x3xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>) {
// CHECK:  "tf.VarHandleOp"
// CHECK-NOT:  [[VARIABLE:%.*]] = "tf.ReadVariableOp"

  %outputs_2 = "tf.add"(%outputs_0, %outputs_1): (tensor<1x1xf32>, tensor<1x1xf32>) -> tensor<1x1xf32>
  return %outputs_2 : tensor<1x1xf32>
}

// CHECK-LABEL: @_func
func.func private @_func(%arg0: tensor<1x3xf32>, %arg1: tensor<3x1xf32>) -> (tensor<1x1xf32>) {

func.func @mean_4d_no_keepdim(%arg0: tensor<1x48x48x512xf32>) -> tensor<1x512xf32> {
  %cst = arith.constant dense<[1, 2]> : tensor<2xi32>
  %0 = "tfl.mean"(%arg0, %cst) {keep_dims = false} : (tensor<1x48x48x512xf32>, tensor<2xi32>) -> tensor<1x512xf32>
  func.return %0 : tensor<1x512xf32>
}

// CHECK:       func @mean_4d_no_keepdim([[VAL_0:%.*]]: tensor<1x48x48x512xf32>) -> tensor<1x512xf32> {

  %1 = "tf.BroadcastTo"(%arg1, %cst) : (tensor<5x1xf32>, tensor<2xi64>) -> tensor<5x7xf32>
  %2 = "tf.Add"(%0, %1) : (tensor<5x7xf32>, tensor<5x7xf32>) -> tensor<5x7xf32>
  func.return %2 : tensor<5x7xf32>
  // CHECK: %[[V0:.*]] = "tf.Add"(%arg0, %arg1) : (tensor<7xf32>, tensor<5x1xf32>) -> tensor<5x7xf32>
  // CHECK: %[[V0]] : tensor<5x7xf32>
}

// CHECK-LABEL: @broadcast_batch_matmul_v2_rhs

  %flow_1 = "tf.TensorArrayWriteV3"(%handle, %index, %value, %flow) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<2x!tf_type.resource<tensor<1x512xf32>>>, tensor<i32>, tensor<1x512xf32>, tensor<f32>) -> tensor<f32>