Update compiler.py f76f151

Co-authored-by: ivy-dev-bot <hello@transpile-ai.com>

binaries.json

@@ -96,6 +96,7 @@
                     "V",
                     "VC",
                     "VCC",
+                    "VCD",
                     "VCI",
                     "VCL",
                     "VCV",
@@ -146,4 +147,4 @@
             }
         ]
     }
-}
+}

ivy/compiler/compiler.py

@@ -2,15 +2,15 @@
 
 
 def clear_graph_cache():
-    """Clears the graph cache which gets populated if `graph_caching` is set to
-    `True` in `ivy.trace_graph`, `ivy.transpile` or `ivy.unify`. Use this to
+    """Clears the graph cache which gets populated if `graph_caching` is set
+    to `True` in `ivy.trace_graph`, `ivy.transpile` or `ivy.unify`. Use this to
     reset or clear the graph cache if needed.
 
     Examples
     --------
     >>> import ivy
-    >>> ivy.clear_graph_cache()
-    """
+    >>> ivy.clear_graph_cache()"""
+
     from ._compiler import clear_graph_cache as _clear_graph_cache
 
     return _clear_graph_cache()
@@ -55,8 +55,8 @@ def graph_transpile(
 
     Returns
     -------
-    Either a transpiled Graph or a non-initialized LazyGraph.
-    """
+    Either a transpiled Graph or a non-initialized LazyGraph."""
+
     from ._compiler import graph_transpile as _graph_transpile
 
     return _graph_transpile(
@@ -93,16 +93,14 @@ def source_to_source(
     e.g. (source="torch_frontend", target="ivy") or (source="torch_frontend", target="tensorflow") etc.
 
     Args:
-    ----
         object: The object (class/function) to be translated.
         source (str, optional): The source framework. Defaults to 'torch'.
         target (str, optional): The target framework. Defaults to 'tensorflow'.
         profiling: Whether to add performance profiling.
 
     Returns:
-    -------
-    The translated object.
-    """
+        The translated object."""
+
     from ._compiler import source_to_source as _source_to_source
 
     return _source_to_source(
@@ -133,8 +131,7 @@ def trace_graph(
     params_v=None,
     v=None
 ):
-    """Takes `fn` and traces it into a more efficient composition of backend
-    operations.
+    """Takes `fn` and traces it into a more efficient composition of backend operations.
 
     Parameters
     ----------
@@ -204,8 +201,8 @@ def trace_graph(
     >>> start = time.time()
     >>> graph(x)
     >>> print(time.time() - start)
-    0.0001785755157470703
-    """
+    0.0001785755157470703"""
+
     from ._compiler import trace_graph as _trace_graph
 
     return _trace_graph(
@@ -231,7 +228,11 @@ def trace_graph(
 
 
 def transpile(
-    object, source: str = "torch", target: str = "tensorflow", profiling: bool = False
+    object,
+    source: str = "torch",
+    target: str = "tensorflow",
+    profiling: bool = False,
+    reuse_existing: bool = True,
 ):
     """Converts a given object (class/function) from one framework to another.
 
@@ -242,23 +243,27 @@ def transpile(
     e.g. (source="torch_frontend", target="ivy") or (source="torch_frontend", target="tensorflow") etc.
 
     Args:
-    ----
         object: The object (class/function) to be translated.
         source (str, optional): The source framework. Defaults to 'torch'.
         target (str, optional): The target framework. Defaults to 'tensorflow'.
         profiling: Whether to add performance profiling.
+        reuse_existing (bool, optional): If True, the function will check if `object`
+                                         already exists in the translated directory and reuse it.
+                                         If False, it will re-translate `object`,
+                                         even if it already exists in the directory, and overwrite
+                                         the old implementation. Defaults to 'True'.
 
     Returns:
-    -------
-    The translated object.
-    """
+        The translated object."""
+
     from ._compiler import transpile as _transpile
 
     return _transpile(
         object=object,
         source=source,
         target=target,
         profiling=profiling,
+        reuse_existing=reuse_existing,
     )