def type(self, type=None, tensorCache=None):
if type is None:
return self._type
self._gradBuffer = self._gradBuffer.type(type)
self.gradInput = self.gradInput.type(type)
self.output = self.output.type(type)
# These casts apply when switching between cuda/non-cuda types
if type != 'torch.cuda.FloatTensor':
self._maskIndexBuffer = self._maskIndexBuffer.long()
self._maskIndices = self._maskIndices.long()
self._gradMask = self._gradMask.byte()
else:
self._maskIndexBuffer = self._maskIndexBuffer.cuda()
self._maskIndices = self._maskIndices.cuda()
self._gradMask = self._gradMask.cuda()
self._type = type
return self
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