diff --git a/test/test_operations.py b/test/test_operations.py
index 545831acf49..fc6765d0483 100644
--- a/test/test_operations.py
+++ b/test/test_operations.py
@@ -793,6 +793,30 @@ def test_sgn(self):
     xla_a = t.to(xla_device).sgn()
     self.assertEqual(a.data, xla_a.data.cpu())
 
+  @skipIfFunctionalizationDisabled("view_as_real unsupported")
+  def test_view_as_real_c64(self):
+    xla_device = torch_xla.device()
+    x = torch.randn(4, dtype=torch.cfloat, device=xla_device)
+    real = torch.view_as_real(x)
+    self.assertEqual(real.dtype, torch.float32)
+    # XLA type of the real needs to be f32 as well
+    self.assertIn("f32[4,2]", torch_xla._XLAC._get_xla_tensor_debug_info(real))
+    # HLO generated needs to have type f32 as well
+    self.assertIn("f32[4,2]",
+                  torch_xla._XLAC._get_xla_tensors_text([real]).split('\n')[-3])
+
+  @skipIfFunctionalizationDisabled("view_as_real unsupported")
+  def test_view_as_real_c128(self):
+    xla_device = torch_xla.device()
+    x = torch.randn(4, dtype=torch.cdouble, device=xla_device)
+    real = torch.view_as_real(x)
+    self.assertEqual(real.dtype, torch.float64)
+    # XLA type of the real needs to be f32 as well
+    self.assertIn("f64[4,2]", torch_xla._XLAC._get_xla_tensor_debug_info(real))
+    # HLO generated needs to have type f32 as well
+    self.assertIn("f64[4,2]",
+                  torch_xla._XLAC._get_xla_tensors_text([real]).split('\n')[-3])
+
   def test_index_put(self):
     xla_device = xm.xla_device()
     a = torch.tensor([1, 1, 1, 1]).to(xla_device).to(dtype=torch.float32)
diff --git a/torch_xla/csrc/ops/ops.cpp b/torch_xla/csrc/ops/ops.cpp
index c8d46edc311..84a3f623200 100644
--- a/torch_xla/csrc/ops/ops.cpp
+++ b/torch_xla/csrc/ops/ops.cpp
@@ -998,11 +998,24 @@ torch::lazy::NodePtr ViewAsRealCopy(const torch::lazy::Value& input) {
     return node.ReturnOp(BuildStack({real, imag}, input_shape.rank()), loctx);
   };
 
-  xla::Shape result_shape = GetXlaShape(input);
-  result_shape.add_dimensions(2);
+  xla::Shape input_shape = GetXlaShape(input);
+  xla::Shape res_shape;
+  switch (input_shape.element_type()) {
+    case xla::PrimitiveType::C64:
+      res_shape = xla::ShapeUtil::MakeShape(xla::PrimitiveType::F32,
+                                            input_shape.dimensions());
+      break;
+    case xla::PrimitiveType::C128:
+      res_shape = xla::ShapeUtil::MakeShape(xla::PrimitiveType::F64,
+                                            input_shape.dimensions());
+      break;
+    default:
+      XLA_ERROR() << "input shape type not supported: " << input_shape;
+  }
+  res_shape.add_dimensions(2);
 
   return GenericOp(torch::lazy::OpKind(at::aten::view_as_real_copy), {input},
-                   result_shape, std::move(lower_fn));
+                   res_shape, std::move(lower_fn));
 }
 
 torch::lazy::NodePtr Rsub(const torch::lazy::Value& input,
diff --git a/torch_xla/csrc/tensor_methods.cpp b/torch_xla/csrc/tensor_methods.cpp
index 9c34f84c44a..f73b5ebb3cf 100644
--- a/torch_xla/csrc/tensor_methods.cpp
+++ b/torch_xla/csrc/tensor_methods.cpp
@@ -3567,7 +3567,7 @@ XLATensorPtr view_as_complex_copy(const XLATensorPtr& input) {
 
 XLATensorPtr view_as_real_copy(const XLATensorPtr& input) {
   return input->CreateFrom(ViewAsRealCopy(input->GetIrValue()),
-                           at::ScalarType::Float);
+                           /*logical_element_type=*/std::nullopt);
 }
 
 XLATensorPtr var(const XLATensorPtr& input, std::vector<int64_t> dimensions,