Breaking Changes:

• The output dimension of neural networks for binary classification tasks is now
  expected to be 1 and not 2 as before. The behavior of nn("head") was also changed to match this.
  This means that for binary classification tasks, t_loss("cross_entropy") now generates
  nn_bce_with_logits_loss instead of nn_cross_entropy_loss.
  This also came with a reparametrization of the t_loss("cross_entropy") loss (thanks to @tdhock, #374).

New Features:

PipeOps & Learners:

• Added po("nn_identity")
• Added po("nn_fn") for calling custom functions in a network.
• Added the FT Transformer model for tabular data.
• Added encoders for numericals and categoricals
• nn("block") (which allows to repeat the same network segment multiple
  times) now has an extra argument trafo, which allows to modify the
  parameter values per layer.

Callbacks:

• The context for callbacks now includes the network prediction (y_hat).
• The lr_one_cycle callback now infers the total number of steps.
• Progress callback got argument digits for controlling the precision
  with which validation/training scores are logged.

Other:

• TorchIngressToken now also can take a Selector as argument features.
• Added function lazy_shape() to get the shape of a lazy tensor.
• Better error messages for MLP and TabResNet learners.
• TabResNet learner now supports lazy tensors.
• The LearnerTorch base class now supports the private method $.ingress_tokens(task, param_vals)
  for generating the torch::dataset.
• Shapes can now have multiple NAs and not only the batch dimension can be missing. However, most nn() operators
  still expect only one missing values and will throw an error if multiple dimensions are unknown.
• Training now does not fail anymore when encountering a missing value
  during validation but uses NA instead.
• It is now possible to specify parameter groups for optimizers via the
  param_groups parameter.