Using PyTorch3D heterogeneous batches and PyTorch optimizers
In this section, we are going to learn how to use the PyTorch optimizer on PyTorch3D heterogeneous mini-batches. In deep learning, we are usually given a list of data examples, such as the following ones – 
.. Here, 
are the observations and 
are the prediction values. For example, 
may be some images and 
the ground-truth classification results – for example, “cat” or “dog”. A deep neural network is then trained so that the outputs of the neural networks are as close to 
as possible. Usually, a loss function between the neural network outputs and 
is defined so that the loss function values decrease as the neural network outputs become closer to .
Thus, training a deep learning network is usually done by minimizing the loss function that is evaluated on all training data examples, 
and
. A straightforward method used in many optimization algorithms is computing the gradients first...
