diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
index f1a7f9a8fc..52243554c8 100644
--- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
+++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
@@ -1093,7 +1093,7 @@ def aten_ops_clone_copy_dtype(
         name,
         args[0],
         kwargs.get("dtype", args[0].dtype),
-        force_layer=True,
+        force_layer=False,
     )
 
 
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/prelu.py b/py/torch_tensorrt/dynamo/conversion/impl/prelu.py
index 166ce16367..f489ccb503 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/prelu.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/prelu.py
@@ -2,6 +2,7 @@
 
 from torch.fx.node import Target
 from torch_tensorrt.dynamo._SourceIR import SourceIR
+from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion.converter_utils import set_layer_name
 from torch_tensorrt.dynamo.types import TRTTensor
@@ -15,6 +16,12 @@ def prelu(
     input: TRTTensor,
     weight: TRTTensor,
 ) -> TRTTensor:
-    layer = ctx.net.add_parametric_relu(input, weight)
+    # TRT requires that the slopes tensor must be unidirectional broadcastable to the input tensor:
+    # the rank of the two tensors must be the same, and all dimensions of the slopes tensor must
+    # either equal the input tensor or be 1. The output tensor has the same shape as the input tensor.
+    input, weight = impl.elementwise.broadcast(
+        ctx, input, weight, f"{name}_broadcast_input", f"{name}_broadcast_weight"
+    )
+    layer = ctx.net.add_parametric_relu(input, slopes=weight)
     set_layer_name(layer, target, name, source_ir)
     return layer.get_output(0)