diff --git a/tests/test_code_replacement.py b/tests/test_code_replacement.py
index 678d44b8f..8c53cb5b1 100644
--- a/tests/test_code_replacement.py
+++ b/tests/test_code_replacement.py
@@ -48,7 +48,10 @@ print("Hello world")
     function_name: str = "NewClass.new_function"
     preexisting_functions: list[str] = ["NewClass.new_function"]
     new_code: str = replace_functions_in_file(
-        original_code, [function_name], optim_code, preexisting_functions,
+        original_code,
+        [function_name],
+        optim_code,
+        preexisting_functions,
     )
     assert new_code == expected
 
@@ -336,7 +339,10 @@ def blab(st):
 print("Not cool")
 """
     new_main_code: str = replace_functions_in_file(
-        original_code_main, ["other_function"], optim_code, ["other_function", "yet_another_function", "blob"]
+        original_code_main,
+        ["other_function"],
+        optim_code,
+        ["other_function", "yet_another_function", "blob"],
     )
     assert new_main_code == expected_main
 
@@ -344,3 +350,195 @@ print("Not cool")
         original_code_dependent, ["blob"], optim_code, []
     )
     assert new_dependent_code == expected_dependent
+
+
+def test_test_libcst_code_replacement7():
+    optim_code = """@register_deserializable
+class CacheSimilarityEvalConfig(BaseConfig):
+
+    def __init__(
+        self,
+        strategy: Optional[str] = "distance",
+        max_distance: Optional[float] = 1.0,
+        positive: Optional[bool] = False,
+    ):
+        self.strategy = strategy
+        self.max_distance = max_distance
+        self.positive = positive
+
+    @staticmethod
+    def from_config(config: Optional[dict[str, Any]]):
+        if config is None:
+            return CacheSimilarityEvalConfig()
+
+        strategy = config.get("strategy", "distance")
+        max_distance = config.get("max_distance", 1.0)
+        positive = config.get("positive", False)
+
+        return CacheSimilarityEvalConfig(strategy, max_distance, positive)
+"""
+
+    original_code = """from typing import Any, Optional
+
+from embedchain.config.base_config import BaseConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class CacheSimilarityEvalConfig(BaseConfig):
+
+    def __init__(
+        self,
+        strategy: Optional[str] = "distance",
+        max_distance: Optional[float] = 1.0,
+        positive: Optional[bool] = False,
+    ):
+        self.strategy = strategy
+        self.max_distance = max_distance
+        self.positive = positive
+
+    @staticmethod
+    def from_config(config: Optional[dict[str, Any]]):
+        if config is None:
+            return CacheSimilarityEvalConfig()
+        else:
+            return CacheSimilarityEvalConfig(
+                strategy=config.get("strategy", "distance"),
+                max_distance=config.get("max_distance", 1.0),
+                positive=config.get("positive", False),
+            )
+
+
+@register_deserializable
+class CacheInitConfig(BaseConfig):
+
+    def __init__(
+        self,
+        similarity_threshold: Optional[float] = 0.8,
+        auto_flush: Optional[int] = 20,
+    ):
+        if similarity_threshold < 0 or similarity_threshold > 1:
+            raise ValueError(f"similarity_threshold {similarity_threshold} should be between 0 and 1")
+
+        self.similarity_threshold = similarity_threshold
+        self.auto_flush = auto_flush
+
+    @staticmethod
+    def from_config(config: Optional[dict[str, Any]]):
+        if config is None:
+            return CacheInitConfig()
+        else:
+            return CacheInitConfig(
+                similarity_threshold=config.get("similarity_threshold", 0.8),
+                auto_flush=config.get("auto_flush", 20),
+            )
+
+
+@register_deserializable
+class CacheConfig(BaseConfig):
+
+    def __init__(
+        self,
+        similarity_eval_config: Optional[CacheSimilarityEvalConfig] = CacheSimilarityEvalConfig(),
+        init_config: Optional[CacheInitConfig] = CacheInitConfig(),
+    ):
+        self.similarity_eval_config = similarity_eval_config
+        self.init_config = init_config
+
+    @staticmethod
+    def from_config(config: Optional[dict[str, Any]]):
+        if config is None:
+            return CacheConfig()
+        else:
+            return CacheConfig(
+                similarity_eval_config=CacheSimilarityEvalConfig.from_config(config.get("similarity_evaluation", {})),
+                init_config=CacheInitConfig.from_config(config.get("init_config", {})),
+            )
+"""
+    expected = """from typing import Any, Optional
+
+from embedchain.config.base_config import BaseConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class CacheSimilarityEvalConfig(BaseConfig):
+
+    def __init__(
+        self,
+        strategy: Optional[str] = "distance",
+        max_distance: Optional[float] = 1.0,
+        positive: Optional[bool] = False,
+    ):
+        self.strategy = strategy
+        self.max_distance = max_distance
+        self.positive = positive
+
+    @staticmethod
+    def from_config(config: Optional[dict[str, Any]]):
+        if config is None:
+            return CacheSimilarityEvalConfig()
+
+        strategy = config.get("strategy", "distance")
+        max_distance = config.get("max_distance", 1.0)
+        positive = config.get("positive", False)
+
+        return CacheSimilarityEvalConfig(strategy, max_distance, positive)
+
+
+@register_deserializable
+class CacheInitConfig(BaseConfig):
+
+    def __init__(
+        self,
+        similarity_threshold: Optional[float] = 0.8,
+        auto_flush: Optional[int] = 20,
+    ):
+        if similarity_threshold < 0 or similarity_threshold > 1:
+            raise ValueError(f"similarity_threshold {similarity_threshold} should be between 0 and 1")
+
+        self.similarity_threshold = similarity_threshold
+        self.auto_flush = auto_flush
+
+    @staticmethod
+    def from_config(config: Optional[dict[str, Any]]):
+        if config is None:
+            return CacheInitConfig()
+        else:
+            return CacheInitConfig(
+                similarity_threshold=config.get("similarity_threshold", 0.8),
+                auto_flush=config.get("auto_flush", 20),
+            )
+
+
+@register_deserializable
+class CacheConfig(BaseConfig):
+
+    def __init__(
+        self,
+        similarity_eval_config: Optional[CacheSimilarityEvalConfig] = CacheSimilarityEvalConfig(),
+        init_config: Optional[CacheInitConfig] = CacheInitConfig(),
+    ):
+        self.similarity_eval_config = similarity_eval_config
+        self.init_config = init_config
+
+    @staticmethod
+    def from_config(config: Optional[dict[str, Any]]):
+        if config is None:
+            return CacheConfig()
+        else:
+            return CacheConfig(
+                similarity_eval_config=CacheSimilarityEvalConfig.from_config(config.get("similarity_evaluation", {})),
+                init_config=CacheInitConfig.from_config(config.get("init_config", {})),
+            )
+"""
+
+    function_names: list[str] = ["CacheSimilarityEvalConfig.from_config"]
+    preexisting_functions: list[str] = [
+        "CacheSimilarityEvalConfig.__init__",
+        "CacheSimilarityEvalConfig.from_config",
+    ]
+    new_code: str = replace_functions_in_file(
+        original_code, function_names, optim_code, preexisting_functions
+    )
+    assert new_code == expected