chore(//docsrc): Update docs deps to fix docgen

docsrc/requirements.txt

@@ -1,6 +1,6 @@
-sphinx==3.1.2
-breathe==4.19.2
-exhale
-sphinx_rtd_theme==0.4.3
+sphinx==4.3.0
+breathe==4.31.0
+exhale==0.2.3
+sphinx_rtd_theme==4.3.0
 sphinx-material==0.0.35
-nbsphinx==0.8.6
+nbsphinx==0.8.7

docsrc/tutorials/getting_started_with_cpp_api.rst

@@ -15,7 +15,7 @@ Please refer to `Creating TorchScript modules in Python <https://nvidia.github.i
 .. _torch_tensorrt_quickstart:
 
 [Torch-TensorRT Quickstart] Compiling TorchScript Modules with ``torchtrtc``
----------------------------------------------------------------------
+---------------------------------------------------------------------------------
 
 An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
 ``torchtrtc``, which supports almost all features of the compiler from the command line including post training quantization

docsrc/tutorials/runtime.rst

@@ -1,14 +1,14 @@
 .. _runtime:
 
 Deploying Torch-TensorRT Programs
-===========================
+====================================
 
 After compiling and saving Torch-TensorRT programs there is no longer a strict dependency on the full
 Torch-TensorRT library. All that is required to run a compiled program is the runtime. There are therfore a couple
 options to deploy your programs other than shipping the full Torch-TensorRT compiler with your applications.
 
 Torch-TensorRT package / libtorchtrt.so
----------------------------------
+--------------------------------------------
 
 Once a program is compiled, you run it using the standard PyTorch APIs. All that is required is that the package
 must be imported in python or linked in C++.