}
    }
    node_def {
      name: "add_0"
      op: "AddV2"
      input: "add_const"
      input: "add/y:output:0"
      attr {
        key: "T"
        value {
          type: DT_INT32
        }
      }
      experimental_debug_info {
        original_node_names: "add"
      }
    }
    ret {
      key: "add"
      value: "add_0:z:0"
    }
    arg_attr {
      key: 0

  VerifyFDef({"add1_0", "add2_0"}, M({{"feed1"}, {"feed2"}, {"feed3"}}),
             M({{"add1"}, {"add2"}}),
             {{"feed1", "add1_0:0"},
              {"feed2", "add1_0:1"},
              {"add1_0:sum:0", "add2_0:0"},
              {"feed3", "add2_0:1"},
              {"add1_0:sum:0", "add1"},
              {"add2_0:sum:0", "add2"}},
             {});
}

  %2 = stablehlo.add %1, %0 : tensor<1x3x2x4xf32>
  return %2 : tensor<1x3x2x4xf32>
}
// CHECK: %[[TRANSPOSE_0:.+]] = stablehlo.transpose
// CHECK: %[[ADD_0:.+]] = stablehlo.add %[[TRANSPOSE_0]], {{.*}}
// CHECK: return %[[ADD_0]]

// -----

// [No change] Tests that the activation transpose whose rank is not 4 is not
// deferred.

// CHECK-LABEL: add_with_activation_transpose_rank_two

    "tf.AssignVariableOp"(%var_0, %cst_0) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
    %add_0 = "tf.Identity"(%cst_0) : (tensor<2xf32>) -> tensor<2xf32>
    %var_1 = "tf.VarHandleOp"() {shared_name = "var_1"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
    "tf.AssignVariableOp"(%var_1, %add_0) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
    return
  }

// CHECK: %[[ADD_0:.+]] = stablehlo.add %[[DOT_GENERAL_0]], %[[ARG_3]] : tensor<1x3x!quant.uniform<i32:f32:1, {{.*}}>>
// CHECK: %[[UNIFORM_QUANTIZE_1:.+]] = stablehlo.uniform_quantize %[[ADD_0]] : (tensor<1x3x!quant.uniform<i32:f32:1, {{.*}}>>) -> tensor<1x3x!quant.uniform<i8:f32, {{.*}}>>

  [(IsNotInLiftedFunc $res),
   (FloatValueEquals<"0"> $cst), (IsStableHLOConstantOp $bias), (AreTheSameValue $conv_0, $conv_1), (AreTheSameValue $add_0, $add_1)], [], (addBenefit 15)>;

def LiftDotGeneralWithBiasAndReluDynamic : Pat<
  (StableHLO_MaxOp:$res
    (StableHLO_AddOp:$add_0
      (StableHLO_DotGeneralOp:$dot_general_0 $lhs, $rhs, $dot_dimension_numbers, $precision_config),
      (StableHLO_DynamicBroadcastInDimOp

  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0: tensor<1xf32>
// CHECK: %[[ADD_0:.*]] = "tf.AddV2"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK: return %[[ADD_0]] : tensor<1xf32>
}

// CHECK-LABEL: softmax
func.func @softmax(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> {

// CHECK-DAG: %[[READ_VAR_1:.*]] = "tf.ReadVariableOp"(%[[VAR_HANDLE_3]]) : (tensor<!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>
// CHECK-DAG: %[[ADD_0:.*]] = "tf.AddV2"(%[[READ_VAR_0]], %[[READ_VAR_1]])
// CHECK: return %[[ADD_0]] : tensor<8xf32>
}

// -----

// Tests the case when there's a tf_saved_model.session_initializer and an empty init function.
module attributes {tf_saved_model.semantics} {

  ops::Switch switch_b(root.WithOpName("switch_b"), value, cond_b);

  Output add_a = ops::Add(root.WithOpName("add_a"), switch_a.output_true,
                          switch_b.output_true);
  Output add_b = ops::Add(root.WithOpName("add_b"), switch_a.output_true,
                          switch_b.output_true);
  Output add = ops::Add(root.WithOpName("add_c"), add_a, add_b);

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));

// Other attributes should be left untouched.
// CHECK-SAME: attributes {tf.entry_function = {inputs = "add_x:0,add_y:0", outputs = "add:0"}}

// Test the newly created "main" function.
// CHECK: func.func @main(%[[ARG0:.*]]: tensor<1xf32> {tf_saved_model.index_path = ["add_x:0"]}, %[[ARG1:.*]]: tensor<1xf32> {tf_saved_model.index_path = ["add_y:0"]})
// CHECK-SAME: -> (tensor<1xf32> {tf_saved_model.index_path = ["add:0"]})