[bug] MixtureOfDiagNormals

[cyianor]

Problem description

I recently tried to use MixtureOfDiagNormals and ran across two issues while using it together with an AutoNormal guide.

1. The distributions MixtureOfDiagNormal and MixtureOfDiagNormalsSharedCovariance do not implement the support property, which leads to problems when using the distribution for latent variables and e.g. the AutoNormal guide. This can be solved easily by adding support = constraints.real_vector at the beginning of both classes. I assume that real_vector is the correct constraint here since D=1 is not supported anyways.
2. There seems to be a bug in the forward method of _MixDiagNormalSample. Specifically, when creating the white noise

       white = locs.new(noise_shape).normal_()

   This creates a new tensor of size (len(noise_shape),) instead of expected size noise_shape. This is actually done correctly in _MixDiagNormalSharedCovarianceSample with a call to torch.randn instead.
   The correct way to do it would be

       white = locs.new(*noise_shape).normal_()

   However, since torch.Tensor.new does not appear in the PyTorch documentation (at least I didn't find it), I assume it is recommended to use a method like torch.Tensor.new_empty instead.

       white = locs.new_empty(noise_shape).normal_()

   works as expected. Alternatively, the same approach as for MixtureOfDiagNormalSharedCovariance is possible.

Package versions

PyTorch: version 2.0.1
Pyro: version 1.8.6