docstr: Differentiable constructor for MaskedTensor 
docstr_tok: ["Differentiable","constructor","for","MaskedTensor"]
code: def _from_values(data, mask):
      """Differentiable constructor for MaskedTensor"""
  
      class Constructor(torch.autograd.Function):
          @staticmethod
          def forward(ctx, data, mask):
              return MaskedTensor(data, mask)
  
          @staticmethod
          def backward(ctx, grad_output):
              return grad_output, None
  
      result = Constructor.apply(data, mask)
      return result
  
imports: import warnings
  import torch
  from torch.overrides import get_default_nowrap_functions
label_desc: Use image node_id 5 for calling the MaskedTensor obj's underlying member method code with example usage: obj._from_values(data, mask) and returns: result, MaskedTensor, grad_output,  None