docs: Update the docs to include new device API for to_backend

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

docs: Update docs for to_backend API for new device API and new PyTorch
API

Changes the docs to show the new device dictionary API and how to use
the new to backend api (changed from PyTorch 1.6.0)

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

docsrc/tutorials/use_from_pytorch.rst

@@ -32,31 +32,35 @@ at the documentation for the TRTorch ``TensorRTCompileSpec`` API.
 .. code-block:: python
 
     spec = {
-        "forward": trtorch.TensorRTCompileSpec({
-            "input_shapes": [[1, 3, 300, 300]],
-            "op_precision": torch.half,
-            "refit": False,
-            "debug": False,
-            "strict_types": False,
-            "allow_gpu_fallback": True,
-            "device_type": "gpu",
-            "capability": trtorch.EngineCapability.default,
-            "num_min_timing_iters": 2,
-            "num_avg_timing_iters": 1,
-            "max_batch_size": 0,
-        })
-    }
+        "forward":
+            trtorch.TensorRTCompileSpec({
+                "input_shapes": [[1, 3, 300, 300]],
+                "op_precision": torch.half,
+                "refit": False,
+                "debug": False,
+                "strict_types": False,
+                "device": {
+                    "device_type": trtorch.DeviceType.GPU,
+                    "gpu_id": 0,
+                    "allow_gpu_fallback": True
+                },
+                "capability": trtorch.EngineCapability.default,
+                "num_min_timing_iters": 2,
+                "num_avg_timing_iters": 1,
+                "max_batch_size": 0,
+            })
+        }
 
 Now to compile with TRTorch, provide the target module objects and the spec dictionary to ``torch._C._jit_to_tensorrt``
 
 .. code-block:: python
 
-    trt_model = torch._C._jit_to_tensorrt(script_model._c, spec)
+    trt_model = torch._C._jit_to_backend("tensorrt", script_model, spec)
 
 To run explicitly call the function of the method you want to run (vs. how you can just call on the module itself in standard PyTorch)
 
 .. code-block:: python
 
-    input = torch.randn((1, 3, 300, 300).to("cuda").to(torch.half)
+    input = torch.randn((1, 3, 300, 300)).to("cuda").to(torch.half)
     print(trt_model.forward(input))