Returns a 1-D tensor of size ⌈ end − start step ⌉ \left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil ⌈stepend−start⌉ with values from the interval [start, end) taken with common difference step beginning from start.
Note: When using floating-point dtypes (especially reduced precision types like bfloat16), the results may be affected by floating-point rounding behavior. Some values in the sequence might not be exactly representable in certain floating-point formats, which can lead to repeated values or unexpected rounding. For precise sequences, it is recommended to use integer dtypes instead of floating-point dtypes.
Note that non-integer step is subject to floating point rounding errors when comparing against end; to avoid inconsistency, we advise subtracting a small epsilon from end in such cases.
out i + 1 = out i + step \text{out}_{{i+1}} = \text{out}_{i} + \text{step} outi+1=outi+step
start (Number, optional) – the starting value for the set of points. Default: 0.
end (Number) – the ending value for the set of points
step (Number, optional) – the gap between each pair of adjacent points. Default: 1.
out (Tensor, optional) – the output tensor.
dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()). 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.
layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.
device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.
Example:
>>> torch.arange(5) tensor([ 0, 1, 2, 3, 4]) >>> torch.arange(1, 4) tensor([ 1, 2, 3]) >>> torch.arange(1, 2.5, 0.5) tensor([ 1.0000, 1.5000, 2.0000])
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