Tensorboard log_graph does not seem to do anything
🐛 Bug

While exploring Tensorboard's logging features I experimented with the log_graph function, that was added after #2915 (issue) and #3003 (PR).
According to the docs the function has the following signature log_graph(model, input_array=None)
So I tried to call it doing, inside my PL module:
...

def __init__(self, ..):
    ...
    self.example_input_array = torch.rand((10, self.input_dim))
    ...

def test_step(self, batch, batch_idx):
    ...
    self.logger.log_graph(self)
    ...
without any success. No error message is displayed and Tensorboard does not recognise any logged graph.
Nevertheless if I change my code to:
...

def __init__(self, ..):
    ...
    self.example_input_array = torch.rand((10, self.input_dim))
    ...

def test_step(self, batch, batch_idx):
    ...
    self.logger.experiment.add_graph(self, self.example_input_array)
    ...
Which is what the log_graph function is supposed to call, then I do see my graph appearing in Tensorboard.
Please reproduce using the BoringModel and post here

To Reproduce

Execute the following gist
https://gist.github.com/pierresegonne/091a1d0e163f01a6f1c944c1bc4e9cf6
And change lines 123/124 to see the graph being logged or not
Environment
* CUDA:
        - GPU:
        - available:         False
        - version:           None
* Packages:
        - numpy:             1.19.2
        - pyTorch_debug:     True
        - pyTorch_version:   1.7.0
        - pytorch-lightning: 1.0.2
        - tqdm:              4.50.2
* System:
        - OS:                Darwin
        - architecture:
                - 64bit
                - 
        - processor:         i386
        - python:            3.8.6
        - version:           Darwin Kernel Version 19.6.0: Sun Jul  5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64