Returns a 1-D tensor of size ⌊ end − start step ⌋ + 1 \left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1 ⌊stepend−start⌋+1 with values from start to end with step step. Step is the gap between two values in the tensor.
out i + 1 = out i + step . \text{out}_{i+1} = \text{out}_i + \text{step}. outi+1=outi+step.
Warning
This function is deprecated and will be removed in a future release because its behavior is inconsistent with Python’s range builtin. Instead, use torch.arange(), which produces values in [start, end).
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 step 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.range(1, 4) tensor([ 1., 2., 3., 4.]) >>> torch.range(1, 4, 0.5) tensor([ 1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000, 4.0000])
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