Merge human-made docs from README.md to docs/README.md.in

Author: jdh8

docs/README.md.in

@@ -27,7 +27,7 @@ import torch
 import torch_ttnn
 
 # A torch Module
-class FooModule(torch.Module):
+class FooModule(torch.nn.Module):
     ...
 # Create a module
 module = FooModule()
@@ -46,7 +46,7 @@ The tracer dump the information of fx graph such as node's op_name and shape.
 
 For example, you can run this script to parse the information
 ```
-PYTHONPATH=$(pwd) python3 tools/run_torchvision.py --backend torch_stat --backward --profile
+PYTHONPATH=$(pwd) python3 tools/stat_models.py --trace_orig --backward --profile
 ls stat/raw
 ```
 
@@ -74,10 +74,35 @@ The `*_total_*_size_dist/` statistics the `op_type`'s input/output_size distribu
 
 [The `profile/` is the tools provided by pytorch](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html), you can open it by the url: chrome://tracing
 
+
+# For developers
+
+## Install torch-ttnn with editable mode
+
+During development, you may want to use the torch-ttnn package for testing.
+In order to do that, you can install the torch-ttnn package in "editable"
+mode with
+
+```shell
+pip install -e .
+```
+
+Now, you can utilize `torch_ttnn` in your Python code. Any modifications you make to the `torch_ttnn` package will take effect immediately, eliminating the need for constant reinstallation via pip.
+
+## Build wheel file
+
+For developers want to deploy the wheel, you can build the wheel file with
+
+```shell
+python -m build
+```
+
+Then you can upload the `.whl` file to the PyPI (Python Package Index).
+
 ## Run transformer models
 To run transformer model with ttnn backend, run:
 ```
-PYTHONPATH=${{TT_METAL_HOME}}:$(pwd) python3 tools/run_transformers.py --model "phiyodr/bert-large-finetuned-squad2" --backend torch_ttnn
+PYTHONPATH=${TT_METAL_HOME}:$(pwd) python3 tools/run_transformers.py --model "phiyodr/bert-large-finetuned-squad2" --backend torch_ttnn
 ```
 
 You can also substitute the backend with `torch_stat` to run a reference comparison.