# `Torchx.Backend`
[🔗](https://github.com/elixir-nx/nx/blob/v0.12.0/torchx/lib/torchx/backend.ex#L1)

An opaque backend Nx backend with bindings to libtorch/Pytorch.

Torchx behaviour that is different from BinaryBackend:

  1. Torchx emulates the u16/u32/u64 unsigned integers using signed integers.
     In practice this means u64 actually overflows at u32. When accessing the
     underlying tensor, you will get signed integers back.

      iex> t = Nx.Constants.max_finite({:u, 32})
      #Nx.Tensor<
        u32
        4294967295
      >
      iex> t |> Torchx.from_nx() |> Torchx.to_nx()
      #Nx.Tensor<
        s64
        4294967295
      >

  2. Torchx rounds half-to-even, while Elixir rounds half-away-from-zero.
     So in Elixir `round(0.5) == 1.0` while in Torchx `round(0.5) == 0.0`.

  3. `Nx.as_type/2` converts non-finite values such as infinity becomes the
     maximum value for a type, negative infinity becomes the minimum value,
     and nan becomes zero. `Torchx` behaviour is type dependent with no clear
     rule across types.

## Options

  * `:device` - Defaults to `Torchx.default_device/0`. An atom representing the
    device for the allocation of a given tensor. Valid values can be seen at the
    main `Torchx` docs.

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*Consult [api-reference.md](api-reference.md) for complete listing*