1module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 398 : i32}, tf_saved_model.semantics} { 2 "tf_saved_model.global_tensor"() {is_mutable, sym_name = "y", type = tensor<3x1xi32>, value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> () 3 "tf_saved_model.asset"() {sym_name = "z", filename = "file"} : () -> () 4 func.func @serving_default( 5 %arg0: tensor<1x3xi32> {tf_saved_model.index_path = ["x"]}, 6 %arg1: tensor<!tf_type.resource<tensor<3x1xi32>>> {tf_saved_model.bound_input = @y}, 7 %arg2: tensor<!tf_type.string> {tf_saved_model.bound_input = @z} 8 ) -> (tensor<1x1xi32> {tf_saved_model.index_path = ["r"]}) 9 attributes { 10 tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "result:0"}, 11 tf_saved_model.exported_names = ["serving_default"] 12 } 13 { 14 %0 = "tf.ReadVariableOp"(%arg1) {device = ""} : (tensor<!tf_type.resource<tensor<3x1xi32>>>) -> tensor<3x1xi32> 15 %1 = "tf.MatMul"(%arg0, %0) {device = "", transpose_a = false, transpose_b = false} : (tensor<1x3xi32>, tensor<3x1xi32>) -> tensor<1x1xi32> 16 func.return %1 : tensor<1x1xi32> 17 } 18 func.func @predict( 19 ) -> (tensor<0x!tf_type.string> {tf_saved_model.index_path = ["r"]}) 20 attributes { 21 tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "result:0"}, 22 tf_saved_model.exported_names = ["predict"] 23 } 24 { 25 %0 = "tf.Const"() {dtype = !tf_type.string, value = dense<[]> : tensor<0x!tf_type.string>} : () -> tensor<0x!tf_type.string> 26 func.return %0 : tensor<0x!tf_type.string> 27 } 28} 29