// CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"
  // CHECK-SAME: "var1"
  // CHECK: %[[CST:.*]] = arith.constant dense<> : tensor<0xf32>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[CST]])
}

// -----

// Test with an existing session init function.
module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {

    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()
  }) {is_stateless = false} : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)
  func.return %0#1 : tensor<1xf32>
}

func.func @cond(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> tensor<i1> {
  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>

  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1:2 = func.call @WhileOp_body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)
    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()
  }) : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>) loc("WhileOp")
  func.return %0#1 : tensor<1xf32>

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

  %0 = "tfl.pseudo_const" () {value = dense<-1.23697901> : tensor<32xf32>} : () -> tensor<32xf32> loc("Const")

//   start_index_map: 1
// }
func.func @xla_gather(%arg0: tensor<?x2xf32>, %arg1: tensor<1xi32>, %arg2: tensor<2xi32>) -> tensor<*xf32> {
  %0 = "tf.XlaGather"(%arg0, %arg1, %arg2) {device = "", dimension_numbers = "\0A\01\00\12\01\01\1A\01\01", indices_are_sorted = true} : (tensor<?x2xf32>, tensor<1xi32>, tensor<2xi32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK: func @xla_gather

    } : (tensor<*xf32>, tensor<144x10xf32>, tensor<10xf32>) -> tensor<*xf32>
    %5 = "quantfork.qcast"(%4) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.1>>
    %6 = "quantfork.dcast"(%5) : (tensor<*x!quant.uniform<i8:f32, 0.1>>) -> tensor<*xf32>
    func.return %6 : tensor<*xf32>
  }

  func.func private @composite_matmul_with_bias_fn_1(%a: tensor<*xf32>, %b: tensor<*xf32>, %c: tensor<*xf32>) -> tensor<*xf32> {

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x384xf32>
    %cst_1 = arith.constant dense<1.0> : tensor<5x384xf32>
    %0 = "tfl.fully_connected"(%arg0, %cst_0, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<?x384xf32>, tensor<5x384xf32>, tensor<5xf32>) -> tensor<?x5xf32>

    %pred = arith.constant false
    cf.cond_br %pred, ^bb1(%v : tensor<!tf_type.resource<tensor<?xf32>>>), ^bb1(%v1 : tensor<!tf_type.resource<tensor<?xf32>>>)
  ^bb1(%either: tensor<!tf_type.resource<tensor<?xf32>>>):
    %ret = "tf.ReadVariableOp"(%either) : (tensor<!tf_type.resource<tensor<?xf32>>>) -> tensor<?xf32>

  %multiples = "tf.Const"() { device = "/device:CPU:0", value = dense<[7,3]> : tensor<2xi32> } : () -> tensor<2xi32>
  // CHECK: T = !corert.string
  %output = "tf.Tile"(%arg, %multiples) { device = "/device:CPU:0" } : (tensor<1x2x!tf_type.string>, tensor<2xi32>) -> tensor<?x6x!tf_type.string>
  func.return %output : tensor<?x6x!tf_type.string>
}

// CHECK-LABEL: func @shape
func.func @shape() {

func.func @FuseTransposeFCRhsToBatchMatmul(%arg0: tensor<16x1024xf32>, %arg1: tensor<1024x128xf32>, %arg2: none) -> tensor<16x128xf32> {
  %cst = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.transpose"(%arg1, %cst) : (tensor<1024x128xf32>, tensor<2xi32>) -> tensor<128x1024xf32>
  // CHECK: "tfl.batch_matmul"(%arg0, %arg1)