text: Title: Dtype issue in `TorchNormalizer(method="identity")`
  Body: - PyTorch-Forecasting version: 0.10.1
  - PyTorch version: 1.10.2+cu111
  - Python version: 3.8.10
  - Operating System: Ubuntu
  
  I recently experienced a `dtype` issue when using `TorchNormalizer` and managed to identify the source of the bug.
  
  When `TorchNormalizer(method="identity", center=False)` is fitted on pandas DataFrame/Series, the attributes `self.center_`and `self.scale_` are set to `np.zeros(y_center.shape[:-1])` and `np.ones(y_scale.shape[:-1])` respectively (lines 467 and 468 of file `pytorch_forecasting.data.encoders.py`).
  
  The default `dtype` for a numpy array is `np.float64` and so both `self.center_`and `self.scale_` have this dtype independently of the `dtype` of `y_center` and `y_scale`. On the other hand, the default dtype of torch (unless specified otherwise using `torch.set_default_dtype`) is `float32` (a.k.a. `Float`, as opposed to `Double` which is equivalent to `float64`).
  
  This may cause problems at inference time (e.g., with DeepAR): the predicted values have type `float64` while the targets in the dataloader have type `float32` (due to scaling). More specifically, at some point during inference (method `predict`), the model calls the method `self.decode_autoregressive` that iteratively fills a torch tensor using method `self.decode_one` (line 292 of file `pytorch_forecasting.models.deepar.py`). The source tensor has type float32 while the other tensor has type float64.
  
  My suggestion would be to modify the code of `TorchNormalizer`. One possibility would be to set the type of `self.center_`and `self.scale_` to be the same as `y_center`and `y_scale` respectively. The problem is that it may be a bit weird if the type of `y_scale` is `int32` for instance (we expect the scale to be a real value rather than an integer). This happens when using NegativeBinomialLoss for instance. Alternatively, we could set the type of `self.center_`and `self.scale_` to be equal to `str(torch.get_default_dtype())[6:]` (this is the current fix I made in my code but not sure it is the best idea).
  