text: Title: [BUG] PLForecastingModule._calculate_metrics should call log_dict with batch_size
  Body: **Describe the bug**
  When using additional metrics with PyTorch Lightning forecasting models, due to `PLForecastingModule._calculate_metrics` (called from `train_step()` or `validation_step()`) not calling `self.log_dict(...)` with the `batch_size=` parameter, the Trainer (`_ResultCollection.log()` is complaining about ambiguity (when `show_warnings=True` is enabled on the model):
  > /opt/homebrew/Caskroom/miniconda/base/envs/tf/lib/python3.12/site-packages/pytorch_lightning/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 32. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
  
  **To Reproduce**
  Add a custom metric via `torch_metrics=...` model parameter (e.g. BlockRNNModel), and run .fit().
  
  **Expected behavior**
  The warnings should be gone.
  
  **System (please complete the following information):**
   - Python version: 3.12.3
   - darts version: 0.29.0
  
  **Additional context**
  suggested code modification (tested):
  ```python
  def _calculate_metrics(self, output, target, metrics):
      if not len(metrics):
          return
  
      if self.likelihood:
          _metric = metrics(self.likelihood.sample(output), target)
      else:
          # If there's no likelihood, nr_params=1, and we need to squeeze out the
          # last dimension of model output, for properly computing the metric.
          _metric = metrics(output.squeeze(dim=-1), target)
  
      self.log_dict(
          _metric,
          on_epoch=True,
          on_step=False,
          logger=True,
          prog_bar=True,
          sync_dist=True,
          batch_size=target.shape[0],  # ADD THIS LINE
      )
  ```