Clarify torch.arange floating-point rounding behavior (#141655)

Added documentation note clarifying the rounding behavior of `torch.arange` when using floating-point dtypes, particularly for reduced precision types like `bfloat16`. This helps users understand potential issues like repeated values and provides guidance on using integer dtypes for precise sequences.

## Changes
- Added explanatory note about floating-point rounding behavior and its effects
- Included specific mention of `bfloat16` dtype issues
- Added recommendation to use integer dtypes for precise sequences

Fixes [#137774](https://github.com/pytorch/pytorch/issues/137774)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141655
Approved by: https://github.com/cpuhrsch

torch/_torch_docs.py

@@ -9122,6 +9122,12 @@ Returns a 1-D tensor of size :math:`\left\lceil \frac{\text{end} - \text{start}}
 with values from the interval ``[start, end)`` taken with common difference
 :attr:`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 :attr:`step` is subject to floating point rounding errors when
 comparing against :attr:`end`; to avoid inconsistency, we advise subtracting a small epsilon from :attr:`end`
 in such cases.