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# torch.arange

torch.arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

  Returns a 1-D tensor of size ⌈end−start step⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil⌈step end−start​⌉ 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 subtracting a small epsilon from `end` in such cases.

  out i+1=out i+step\text{out}_{{i+1}} = \text{out}_{i} + \text{step} out i+1​=out i​+step
  Parameters:

    - start (Number) – the starting value for the set of points. Default: `0`.

    - end (Number) – the ending value for the set of points

    - step (Number) – the gap between each pair of adjacent points. Default: `1`.

  Keyword Arguments:

    - 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_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.

    - 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_tensor_type()`). `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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