diff --git a/paconvert/api_mapping.json b/paconvert/api_mapping.json
index be111596b..8f451b1c9 100644
--- a/paconvert/api_mapping.json
+++ b/paconvert/api_mapping.json
@@ -3321,6 +3321,7 @@
     "min_input_args": 0,
     "args_list": [
       "dtype",
+      "dst_type",
       "non_blocking"
     ]
   },
diff --git a/paconvert/api_matcher.py b/paconvert/api_matcher.py
index 5eef87d9b..e407f3e69 100644
--- a/paconvert/api_matcher.py
+++ b/paconvert/api_matcher.py
@@ -1563,7 +1563,11 @@ def generate_code(self, kwargs):
         if len(kwargs) == 0:
             code = f"str({self.paddleClass}.dtype)"
         else:
-            code = f"{self.paddleClass}.astype({kwargs['dtype']})"
+            # For torch.nn.Module.type, torch.nn.Module.type use torch.Tensor.type
+            if "dst_type" in kwargs:
+                code = f"{self.paddleClass}.astype({kwargs['dst_type']})"
+            else:
+                code = f"{self.paddleClass}.astype({kwargs['dtype']})"
         return code
 
 
diff --git a/tests/test_Tensor_signbit.py b/tests/test_Tensor_signbit.py
index b5cad33ec..ced4f975f 100644
--- a/tests/test_Tensor_signbit.py
+++ b/tests/test_Tensor_signbit.py
@@ -23,7 +23,7 @@ def test_case_1():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
         result = x.signbit()
         """
     )
@@ -34,7 +34,7 @@ def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float64')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float64)
         result = x.signbit()
         """
     )
diff --git a/tests/test_nn_Module_type.py b/tests/test_nn_Module_type.py
index 06f743210..a38c4acfe 100644
--- a/tests/test_nn_Module_type.py
+++ b/tests/test_nn_Module_type.py
@@ -33,8 +33,8 @@ def test_case_1():
     obj.run(pytorch_code, ["result"])
 
 
-# Will match torch.nn.Module, the named parameter "dst_type" cannot be resolved.
-def _test_case_2():
+# Will match torch.Tensor.type to resolve "dst_type" parameter.
+def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
diff --git a/tests/test_optim_lr_scheduler_CosineAnnealingWarmRestarts.py b/tests/test_optim_lr_scheduler_CosineAnnealingWarmRestarts.py
index 91bfcf098..2e1b4621d 100644
--- a/tests/test_optim_lr_scheduler_CosineAnnealingWarmRestarts.py
+++ b/tests/test_optim_lr_scheduler_CosineAnnealingWarmRestarts.py
@@ -15,14 +15,14 @@
 import textwrap
 
 from apibase import APIBase
-from lr_scheduler_helper import generate_torch_code
+from lr_scheduler_helper import generate_lr_scheduler_test_code
 
 obj = APIBase("torch.optim.lr_scheduler.CosineAnnealingWarmRestarts")
 
 
 def test_case_1():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(sgd, 10)"
         )
     )
@@ -31,7 +31,7 @@ def test_case_1():
 
 def test_case_2():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(sgd, T_0=10)"
         )
     )
@@ -40,7 +40,7 @@ def test_case_2():
 
 def test_case_3():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10)"
         )
     )
@@ -49,7 +49,7 @@ def test_case_3():
 
 def test_case_4():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.0, last_epoch=-1, verbose=True)"
         )
     )
@@ -58,7 +58,7 @@ def test_case_4():
 
 def test_case_5():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.05, verbose=True)"
         )
     )
@@ -67,7 +67,7 @@ def test_case_5():
 
 def test_case_6():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(sgd, 10, 1, 1.0, -1, False)"
         )
     )
@@ -79,7 +79,7 @@ def test_case_6():
 # paddle result has diff with pytorch result
 def test_case_7():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             [
                 "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.0, last_epoch=-1, verbose=False)",
                 "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=sgd, T_0=10, eta_min=0.0, last_epoch=scheduler_1.last_epoch, verbose=False)",
diff --git a/tests/test_optim_lr_scheduler_LinearLR.py b/tests/test_optim_lr_scheduler_LinearLR.py
index f39c8ab8d..8d9c7bb37 100644
--- a/tests/test_optim_lr_scheduler_LinearLR.py
+++ b/tests/test_optim_lr_scheduler_LinearLR.py
@@ -15,21 +15,23 @@
 import textwrap
 
 from apibase import APIBase
-from lr_scheduler_helper import generate_torch_code
+from lr_scheduler_helper import generate_lr_scheduler_test_code
 
 obj = APIBase("torch.optim.lr_scheduler.LinearLR")
 
 
 def test_case_1():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd, verbose=True)")
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.LinearLR(sgd, verbose=True)"
+        )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_2():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(sgd, start_factor=0.05, end_factor=1.0)"
         )
     )
@@ -38,21 +40,25 @@ def test_case_2():
 
 def test_case_3():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd, total_iters=3)")
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.LinearLR(sgd, total_iters=3)"
+        )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_4():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd, 0.05, 1)")
+        generate_lr_scheduler_test_code(
+            "torch.optim.lr_scheduler.LinearLR(sgd, 0.05, 1)"
+        )
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
 
 
 def test_case_5():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3)"
         )
     )
@@ -61,7 +67,7 @@ def test_case_5():
 
 def test_case_6():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(start_factor=0.05, end_factor=1.0, total_iters=3, optimizer=sgd)"
         )
     )
@@ -70,7 +76,7 @@ def test_case_6():
 
 def test_case_7():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(sgd, 0.05, 1.0, 3, -1, False)"
         )
     )
@@ -79,7 +85,7 @@ def test_case_7():
 
 def test_case_8():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3, last_epoch=-1, verbose=False)"
         )
     )
@@ -88,7 +94,7 @@ def test_case_8():
 
 def test_case_9():
     pytorch_code = textwrap.dedent(
-        generate_torch_code(
+        generate_lr_scheduler_test_code(
             [
                 "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3, last_epoch=-1, verbose=False)",
                 "torch.optim.lr_scheduler.LinearLR(optimizer=sgd, start_factor=0.05, end_factor=1.0, total_iters=3, last_epoch=scheduler_1.last_epoch, verbose=False)",
@@ -100,6 +106,6 @@ def test_case_9():
 
 def test_case_10():
     pytorch_code = textwrap.dedent(
-        generate_torch_code("torch.optim.lr_scheduler.LinearLR(sgd)")
+        generate_lr_scheduler_test_code("torch.optim.lr_scheduler.LinearLR(sgd)")
     )
     obj.run(pytorch_code, ["result1", "result2"], rtol=1.0e-5)
diff --git a/tests/test_signbit.py b/tests/test_signbit.py
index 3ecc5aee4..bb87afebc 100644
--- a/tests/test_signbit.py
+++ b/tests/test_signbit.py
@@ -23,7 +23,7 @@ def test_case_1():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
         result = torch.signbit(x)
         """
     )
@@ -34,7 +34,7 @@ def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
         result = torch.signbit(input=x)
         """
     )
@@ -45,8 +45,8 @@ def test_case_3():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
-        out = torch.tensor([])
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
+        out = torch.tensor([], dtype=torch.bool)
         result = torch.signbit(out=out, input=x)
         """
     )
@@ -57,8 +57,8 @@ def test_case_4():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
-        out = torch.tensor([])
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
+        out = torch.tensor([], dtype=torch.bool)
         result = torch.signbit(input=x, out=out)
         """
     )
@@ -69,8 +69,8 @@ def test_case_5():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype='float32')
-        out = torch.tensor([])
+        x = torch.tensor([-0., 1.1, -2.1, 0., 2.5], dtype=torch.float32)
+        out = torch.tensor([], dtype=torch.bool)
         result = torch.signbit(x, out=out)
         """
     )