Targs = [tensor<32xf32>, tensor<64xf32>, tensor<8xf32>, tensor<2xf32>],
      Tresults = [tensor<32xf32>, tensor<64xf32>, tensor<8xf32>]}
      : (tensor<32xf32>, tensor<64xf32>, tensor<8xf32>, tensor<2xf32>, tensor<2x!tf_type.string>)
        -> (tensor<32xf32>, tensor<64xf32>, tensor<8xf32>)
    tf_device.return %0#0, %0#1, %0#2 : tensor<32xf32>, tensor<64xf32>, tensor<8xf32>

      %read1 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
      %var_handle = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      %read2 = "tf.ReadVariableOp"(%var_handle) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>

      "tf.AssignVariableOp"(%arg0, %read2) : (!tf_res, tensor<32xf32>) -> ()
      %read3 = "tf.ReadVariableOp"(%arg0) : (!tf_res) -> tensor<32xf32>
      tf_executor.yield %read3 : tensor<32xf32>
    }
    tf_executor.fetch %island#0 : tensor<32xf32>
  }
  func.return %graph : tensor<32xf32>
}

// -----
// Tests aliasing of the two resource handles that refer to the same variable.

        -> !tf_res
      %read = "tf.ReadVariableOp"(%id2) : (!tf_res) -> tensor<32xf32>
      %id3 = "tf.Identity"(%read) : (tensor<32xf32>) -> tensor<32xf32>
      tf_executor.yield
    }
    tf_executor.fetch %island : !tf_executor.control
  }
  func.return
}

// -----
!tf_res = tensor<*x!tf_type.resource<tensor<32xf32>>>

// Tesets that the pass can propagate through tf.If's branches.

      %read1 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
      %var_handle = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      %read2 = "tf.ReadVariableOp"(%var_handle) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>

    %assign1_control = tf_executor.island wraps "tf.AssignVariableOp"(%arg1, %read0) : (tensor<*x!tf_type.resource<tensor<32xf32>>>, tensor<32xf32>) -> ()
    %assign2_control = tf_executor.island wraps "tf.AssignVariableOp"(%arg0, %read2) : (tensor<*x!tf_type.resource<tensor<32xf32>>>, tensor<32xf32>) -> ()

  %1:2 = "tfl.split"(%cst, %0) {num_splits = 2 : i32} : (tensor<i32>, tensor<4xf32>) -> (tensor<2xf32>, tensor<2xf32>)
  %2 = "tfl.quantize"(%1#0) {qtype = tensor<2x!quant.uniform<u8:f32, 1.0>>} : (tensor<2xf32>) -> tensor<2x!quant.uniform<u8:f32, 1.0>>
  %3 = "tfl.quantize"(%1#1) {qtype = tensor<2x!quant.uniform<u8:f32, 1.0>>} : (tensor<2xf32>) -> tensor<2x!quant.uniform<u8:f32, 1.0>>

  %1:4 = "tfl.split"(%cst, %0) {num_splits = 4 : i32} : (tensor<i32>, tensor<4xf32>) -> (tensor<2xf32>, tensor<2xf32>,tensor<2xf32>, tensor<2xf32>)
  %2 = "tfl.quantize"(%1#0) {qtype = tensor<2x!quant.uniform<u8:f32, 1.0>>} : (tensor<2xf32>) -> tensor<2x!quant.uniform<u8:f32, 1.0>>
  %3 = "tfl.quantize"(%1#1) {qtype = tensor<2x!quant.uniform<u8:f32, 1.0>>} : (tensor<2xf32>) -> tensor<2x!quant.uniform<u8:f32, 1.0>>

  %1 = "tf.XlaSharding"(%0) { _XlaSharding = "\01\02\03", sharding = "\01\02\03" } : (tensor<32xf32>) -> tensor<32xf32>
  %2 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
  %3 = "tf.Identity"(%2) : (tensor<32xf32>) -> tensor<32xf32>
  %4 = "tf.XlaSharding"(%3) { _XlaSharding = "\04\05\06", sharding = "\04\05\06" } : (tensor<32xf32>) -> tensor<32xf32>

// CHECK-LABEL: floor_mod
func.func @floor_mod(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> tensor<5xf32> {
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>
// CHECK: %[[CUSTOM_0:.*]] = "tfl.custom"(%arg0, %arg1) <{custom_code = "FlexFloorMod", custom_option = #tfl<const_bytes : "{{.*}}">}> : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
// CHECK: return %[[CUSTOM_0]] : tensor<5xf32>
}