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torch.stack — PyTorch 2.12 documentation

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dim (int, optional) – dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive). Default: 0 out (Tensor, optional) – the output tensor. Example: Previous torch.squeeze Next torch.swapaxes Built with the PyData Sphinx Theme 0.15.4. previous torch.squeeze next torch.swapaxes Access comprehensive developer documentation for PyTorch Get in-depth tutorials for beginners and advanced developers Find development resources and get your questions answered Stay in touch for updates, event info, and the latest news By submitting this form, I consent to receive marketing emails from the LF and its projects regarding their events, training, research, developments, and related announcements. I understand that I can unsubscribe at any time using the links in the footers of the emails I receive. Privacy Policy By submitting this form, I consent to receive marketing emails from the LF and its projects regarding their events, training, research, developmen

torch.stack # torch. stack ( tensors , dim = 0 , * , out = None ) → Tensor # Concatenates a sequence of tensors along a new dimension. All tensors need to be of the same size. See also torch.cat() concatenates the given sequence along an existing dimension. Parameters : tensors ( sequence of Tensors ) – sequence of tensors to concatenate dim ( int , optional ) – dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive). Default: 0 Keyword Arguments : out ( Tensor , optional ) – the output tensor. Example: >>> x = torch . randn ( 2 , 3 ) >>

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