text: Title: Torch.where is not correctly supported
  Body: ## Summary
  
  I think there is an issue with the support of "torch.where" within "compile_torch_model".
  Torch.where is expecting a bool tensor for the "condition" parameter, while "compile_torch_model" is expecting a float tensor (maybe related to the discrepancy between the supported type of torch.where and numpy.where for the "condition" parameter). 
  It is not possible to compile a torch model using torch.where because then:
  -to compute the trace, torch requires a bool tensor.
  -to quantize the model, concrete ml is expecting a float tensor.
  
  ## Description
  
  - versions affected: concrete-ml 1.6.1
  - python version: 3.9
  - workaround:
  I was able to make it work with a (very bad) workaround:
  in _process_initializer of PostTrainingAffineQuantization (concrete.ml.quantization.post_training), recast "values" variable to numpy.float if array of bool.
  (unfortunately overriding "_check_distribution_is_symmetric_around_zero" is not enough..)
  
  <details><summary>minimal POC to trigger the bug</summary>
  <p>
  
  ```python
  import torch
  from concrete.ml.torch.compile import compile_torch_model
  
      
  class PTQSimpleNet(torch.nn.Module):
      def __init__(self, n_hidden):
          super().__init__()
          self.n_hidden = n_hidden
          self.fc_tot = torch.rand(1, n_hidden) > 0.5
  
      def forward(self, x):
          y = torch.where(self.fc_tot, x, 0.)
          return y
  
  N_FEAT = 32
  
  torch_input = torch.randn(1, N_FEAT)
  torch_model = PTQSimpleNet(N_FEAT)
  
  quantized_module = compile_torch_model(
      torch_model,
      torch_input
  )
  ```
  
  </p>
  </details>
  