Directly update the optimizer learning rate
I have a specific learning rate schedule in mind. It is based on the epoch but differs from the generally available ones I am aware of including StepLR.
Is there something that would perform the equivalent to:
optimizer.set_lr(lr)
or
optimizer.set_param('lr,',lr)
I would then simply invoke that method at the end of each epoch (or possibly even more frequently)
Context: I am using the adam optimizer as so:
    optimizer = torch.optim.Adam(model.parameters(), lr=LrMax, weight_decay=decay) # , betas=(args.beta1, args.beta2)

Update  I found this information https://discuss.pytorch.org/t/change-learning-rate-in-pytorch/14653:
for param_group in optimizer.param_groups:
        param_group['lr'] = lr

Is there a way to ascertain that the adam optimizer being used is employing the new learning rate?

You can do this in this way:
for param_group in optimizer.param_groups:
    param_group['lr'] = lr

