/// <summary>
/// Returns a 1-D tensor of size \(\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor\)
/// with values from the interval [start, end) taken with common difference
/// step beginning from start.
///
/// Note that non-integer step is subject to floating point rounding errors when
/// comparing against end; to avoid inconsistency, we advise adding a small epsilon to end
/// in such cases.
///
/// \[\text{out}_{{i+1}} = \text{out}_{i} + \text{step}
///
/// \]
/// </summary>
/// <param name="end">
/// the ending value for the set of points
/// </param>
/// <param name="@out">
/// the output tensor
/// </param>
/// <param name="dtype">
/// the desired data type of returned tensor.
/// Default: if None, uses a global default (see torch.set_default_tensor_type()). If dtype is not given, infer the data type from the other input
/// arguments. If any of start, end, or stop are floating-point, the
/// dtype is inferred to be the default dtype, see
/// get_default_dtype(). Otherwise, the dtype is inferred to
/// be torch.int64.
/// </param>
/// <param name="layout">
/// the desired layout of returned Tensor.
/// Default: torch.strided.
/// </param>
/// <param name="device">
/// the desired device of returned tensor.
/// Default: if None, uses the current device for the default tensor type
/// (see torch.set_default_tensor_type()). device will be the CPU
/// for CPU tensor types and the current CUDA device for CUDA tensor types.
/// </param>
/// <param name="requires_grad">
/// If autograd should record operations on the
/// returned tensor. Default: False.
/// </param>
public static Tensor arange(int end, Tensor @out = null, Dtype dtype = null, Layout layout = null, Device device = null, bool?requires_grad = false)
=> PyTorch.Instance.arange(0, end, 1, @out: @out, dtype: torch.int64, layout: layout, device: device, requires_grad: requires_grad);
public static Tensor <T> as_tensor <T>(T[] data, Dtype dtype = null, Device device = null)
{
return(tensor(data, dtype, device));
}
public static Tensor <T> tensor <T>(T[] data, Dtype dtype = null, Device device = null, bool?requires_grad = null, bool?pin_memory = null) => PyTorch.Instance.tensor(data, dtype: dtype, device: device, requires_grad: requires_grad, pin_memory: pin_memory);
