diff --git a/src/bindings/python/src/openvino/frontend/pytorch/decoder.py b/src/bindings/python/src/openvino/frontend/pytorch/decoder.py
index e1af35db27da82..a3c56c76133b45 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/decoder.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/decoder.py
@@ -12,6 +12,25 @@
 import torch
 import numpy as np
 import inspect
+import ctypes
+
+def fetch_attr(self_module, target : str):
+    """
+    Fetch an attribute from the ``Module`` hierarchy of ``self.module``.
+
+    Args:
+        target (str): The fully-qualified name of the attribute to fetch
+
+    Return:
+        Any: The value of the attribute.
+    """
+    target_atoms = target.split('.')
+    attr_itr = self_module
+    for i, atom in enumerate(target_atoms):
+        if not hasattr(attr_itr, atom):
+            raise RuntimeError(f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}")
+        attr_itr = getattr(attr_itr, atom)
+    return attr_itr
 
 def make_constant(*args, **kwargs):
     return op.Constant(*args, **kwargs)
@@ -95,6 +114,10 @@ def get_value_from_getattr(getattr_node, self_module):
     "torch.BoolTensor": OVType.boolean,
 }
 
+ov_to_c_type_map = {
+    OVType.f32: ctypes.c_float,
+    OVType.i32: ctypes.c_int,
+}
 
 class TorchScriptPythonDecoder (Decoder):
     def __init__(self, pt_module, graph_element=None, example_input=None, freeze=True):
@@ -662,7 +685,9 @@ def as_constant(self):
             ovshape = PartialShape(ret.size())
             ovtype = pt_to_ov_type_map[ret.type()]
             print(ovshape, ovtype)
-            ov_const = make_constant(ovtype, ovshape.get_shape(), ret.data_ptr())
+            c_type = ctypes.POINTER(ov_to_c_type_map[ovtype])
+            data_c_ptr = ctypes.cast(ret.data_ptr(), c_type)
+            ov_const = op.Constant(ovtype, ovshape.get_shape(), data_c_ptr[:ret.nelement()])
             print('Made constant')
             return ov_const.outputs()
 
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/op_support.py b/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/op_support.py
index 697535d4beb838..a1e9a709285962 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/op_support.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/op_support.py
@@ -161,7 +161,7 @@ def __init__(self):
             "torch.ops.aten.addmm.default": None,
             "_operator.getitem": None,
             "torch.ops.aten.t.default": None,
-            "torch.ops.aten.empty.memory_format": None
+            #"torch.ops.aten.empty.memory_format": None
         }
 
         super().__init__(support_dict)
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/partition.py b/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/partition.py
index 7247f726d849c3..f0178122927bd1 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/partition.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/partition.py
@@ -2,8 +2,8 @@
 
 import torch
 from torch.nn import Module
-from torch.fx import GraphModule
-from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
+from torch.fx import GraphModule, Node
+from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner, Partition
 
 from torch.fx.experimental.proxy_tensor import DecompositionInterpreter
 from torch._decomp import decomposition_table
@@ -29,7 +29,19 @@ def fx_serialize(self, graph_module: GraphModule, *args, **kwargs):
         #DecompositionInterpreter(fx_gm, prim_graph, decomposition_table=aten2aten_decomp).run(*args, **kwargs)
         #prim_module = torch.fx.GraphModule(fx_gm, prim_graph)
         return fx_gm #prim_module
-   
+
+    def add_get_attr_inputs(self, partitions: t.List[Partition]):   
+        #TODO: Find a more efficient way to include input
+        #"get_attr" nodes to the partitions.
+        getattr_to_merge : Dict[Node, Node] = {}
+        for partition in partitions:
+            for pnode in partition.nodes:
+                for pnode_input in pnode.all_input_nodes:
+                    if pnode_input.op in ['get_attr']:
+                        if pnode_input.op not in getattr_to_merge:
+                            getattr_to_merge[pnode_input] = partition
+        for getattr_node, getattr_part in getattr_to_merge.items():
+            getattr_part.add_node(getattr_node)
 
     def make_partitions(self, graph_module: GraphModule) -> GraphModule:
         # entry function for nvFuser backend
@@ -38,7 +50,9 @@ def make_partitions(self, graph_module: GraphModule) -> GraphModule:
         # FX graph based partitioning based on nvfuser supported ops
         partitioner = CapabilityBasedPartitioner(
             graph_module, self.supported_ops, allows_single_node_partition=True)
-        fused_graph_module = partitioner.partition_and_fuse()
+        partitions = partitioner.propose_partitions()
+        self.add_get_attr_inputs(partitions)
+        fused_graph_module = partitioner.fuse_partitions(partitions)
 
         return fused_graph_module
 
diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
index f30fc7334b75b3..1452a587eebde7 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -380,6 +380,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_fx() {
         {"prim::Constant", op::translate_constant},
         {"prim::device", op::translate_constant},
         {"prim::GetAttr", op::translate_get_attr},
+        {"get_attr", op::translate_constant},
         {"prim::If", op::translate_if},
         {"prim::is_cuda", op::return_false_scalar},
         {"prim::ListConstruct", op::translate_list_construct},