+ j/ dRt^RIt^RIt^RIt^RIt^RIHt^RIt]P]P]P]P]P]P]P]P .t]P$]P&]P(]P*.t]P.]P0]P2]P4.t]P8]P:]P<]P>.t ]PB.]Ot".]O] Ot#RRlt$RRRRRRR RR RR R/R R llt%R#)z1 This module contains tensor creation utilities. N)castcpV^8dQhR\PR\R\R\P/#)tlowhighreturn)torchTensorfloat)formats"X/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/testing/_creation.py __annotate__r$s0%% %5%%%,,%cW!, \P!VP4P8d0VP V^, V^, 4P ^4#VP W4#)r)r finfodtypemaxuniform_mul_)rrrs&&&r _uniform_random_r$sT zU[[)---zz#'4!8,11!44zz#$$rrr requires_gradF noncontiguous exclude_zero memory_formatcV^8dQhR\\P,\\,,\\R3,,R\P R\ \P,R\R,R\R,R\R \R \R \PR,R \P/ #) rshape.rdevicerNrrrrrr) intr Sizelisttuplerstrrr boolrr )r s"r rr-sgg %** tCy (5c? :g ;;g %,, g  g $, g  ggg&&-g \\grc aRV3Rllp \V4^8Xd;\V^,\PP4'd V^,p\ \ \R3,\ V44pV'dVe\RV: RV: 24hV'dS\9d\RS: 24hT;'d\P!RV^4^8pV'd4\ \ \R3,.VRRO^VR,,N54pS\PJdK\ \ \\3,V !VV^^^^R 74wr#\P!W#WSR 7p EMS\9d\ \ \\3,T !TT\P!S4P \P!S4P"S\P$Jd^M^,R^ R 74wr#\P!W#WSR 7p EMGS\&9dV !VV\P(!S4P \P(!S4P"R^ R 7wr#\P*!WSR 7p \-S\.9d\P0!V 4MT W#4MS\29dV !VV\P(!S4P \P(!S4P"R^ R 7wr#\P*!W\P4R 7p \-WV4V P7S4p M\9R S R 24hV'd V R,p MVeV P;VR 7p V'd3S\9d^M\P(!S4P<W^8H&S\&9dWJnV #)aCreates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with values uniformly drawn from ``[low, high)``. If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable finite values then they are clamped to the lowest or highest representable finite value, respectively. If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`, which depend on :attr:`dtype`. +---------------------------+------------+----------+ | ``dtype`` | ``low`` | ``high`` | +===========================+============+==========+ | boolean type | ``0`` | ``2`` | +---------------------------+------------+----------+ | unsigned integral type | ``0`` | ``10`` | +---------------------------+------------+----------+ | signed integral types | ``-9`` | ``10`` | +---------------------------+------------+----------+ | floating types | ``-9`` | ``9`` | +---------------------------+------------+----------+ | complex types | ``-9`` | ``9`` | +---------------------------+------------+----------+ Args: shape (Tuple[int, ...]): Single integer or a sequence of integers defining the shape of the output tensor. dtype (:class:`torch.dtype`): The data type of the returned tensor. device (Union[str, torch.device]): The device of the returned tensor. low (Optional[Number]): Sets the lower limit (inclusive) of the given range. If a number is provided it is clamped to the least representable finite value of the given dtype. When ``None`` (default), this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``. high (Optional[Number]): Sets the upper limit (exclusive) of the given range. If a number is provided it is clamped to the greatest representable finite value of the given dtype. When ``None`` (default) this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``. .. deprecated:: 2.1 Passing ``low==high`` to :func:`~torch.testing.make_tensor` for floating or complex types is deprecated since 2.1 and will be removed in 2.3. Use :func:`torch.full` instead. requires_grad (Optional[bool]): If autograd should record operations on the returned tensor. Default: ``False``. noncontiguous (Optional[bool]): If `True`, the returned tensor will be noncontiguous. This argument is ignored if the constructed tensor has fewer than two elements. Mutually exclusive with ``memory_format``. exclude_zero (Optional[bool]): If ``True`` then zeros are replaced with the dtype's small positive value depending on the :attr:`dtype`. For bool and integer types zero is replaced with one. For floating point types it is replaced with the dtype's smallest positive normal number (the "tiny" value of the :attr:`dtype`'s :func:`~torch.finfo` object), and for complex types it is replaced with a complex number whose real and imaginary parts are both the smallest positive normal number representable by the complex type. Default ``False``. memory_format (Optional[torch.memory_format]): The memory format of the returned tensor. Mutually exclusive with ``noncontiguous``. Raises: ValueError: If ``requires_grad=True`` is passed for integral `dtype` ValueError: If ``low >= high``. ValueError: If either :attr:`low` or :attr:`high` is ``nan``. ValueError: If both :attr:`noncontiguous` and :attr:`memory_format` are passed. TypeError: If :attr:`dtype` isn't supported by this function. Examples: >>> # xdoctest: +SKIP >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) >>> from torch.testing import make_tensor >>> # Creates a float tensor with values in [-1, 1) >>> make_tensor((3,), device="cpu", dtype=torch.float32, low=-1, high=1) >>> # xdoctest: +SKIP tensor([ 0.1205, 0.2282, -0.6380]) >>> # Creates a bool tensor on CUDA >>> make_tensor((2, 2), device="cuda", dtype=torch.bool) tensor([[False, False], [False, True]], device='cuda:0') cV^8dQhR\R,R\R,R\R\R\R\R\\\3,/#) rrNrlowest_inclusivehighest_exclusive default_low default_highr)r r!)r s"r r!make_tensor..__annotate__s_11 T\1dl1 1 ! 1  11 ue| 1rc @<RRlpVeTMTpVeTMTp\;QJdRW34F 'gK RM RM!RW344'd\RV: RV: 24hW8Xd*S\9d\P!R\ ^R 7M>W8d\R V R V 24hW8gW8d\R V R V RS RV R V R2 4hV!WV4pV!WV4pS\ 9d-\P!V4\P!V43#W3#)z~ Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high) if required. cHV^8dQhR\R\R\R\/#)ralhr)r )r s"r r:make_tensor..modify_low_high..__annotate__s( % %U %u % %5 %rc,\\W4V4#N)minr)r-r.r/s&&&r clamp3make_tensor..modify_low_high..clampss1y!$ $rc3~"TF3p\V\4;'d\P!V4xK5 R#5ir2) isinstancer mathisnan).0values& r 7make_tensor..modify_low_high..s+W;%z%'==DJJu,==;s==TFz,`low` and `high` cannot be NaN, but got low=z and high=zPassing `low==high` to `torch.testing.make_tensor` for floating or complex types is deprecated since 2.1 and will be removed in 2.3. Use `torch.full(...)` instead.) stacklevelz(`low` must be less than `high`, but got z >= z5The value interval specified by `low` and `high` is [z, z), but z only supports [)) any ValueError_FLOATING_OR_COMPLEX_TYPESwarningswarn FutureWarning_BOOLEAN_OR_INTEGRAL_TYPESr8ceil)rrr&r'r(r)r4rs&&$$$$ r modify_low_high$make_tensor..modify_low_highsA %_c+'t\ 3WC;W333WC;W W W?3& dWM [U&@@ MM1  [GuDQUPVWX X  $(@GuBtfUg-.>-?rBSATTUW  C+<=T->? . .99S>499T?2 2yr.NzaThe parameters `noncontiguous` and `memory_format` are mutually exclusive, but got noncontiguous=z and memory_format=zU`requires_grad=True` is not supported for boolean and integral dtypes, but got dtype=cW,#r2)xys&&r make_tensor..sAEr)r&r'r(r))rrzThe requested dtype 'z' is not supported by torch.testing.make_tensor(). To request support, file an issue at: https://github.com/pytorch/pytorch/issues)ri).:Nr) lenr7 collectionsabcSequencerr!rrArF functoolsreducer r#randintiinfor3rint64rBremptyr_COMPLEX_TYPES view_as_real_FLOATING_8BIT_TYPESfloat32to TypeErrorclonetinyr) rrrrrrrrrrHresults d$$$$$$$* r make_tensorre-sKd11f 5zQ:eAh 0H0HIIa sCx%, /E2&$&&:M+; =  "<<d^c]e f  "XXi&6&67I5RS&TWX&XMU38_&Bcr &BAb M&BC   #s(O !""#    s%eL , , #s(O !&U!3!7!7"'++e"4"8"8U[[01a #9  "s%eL , ,#  "[[/33#kk%044  U?*/>*AE  v &vs  & &#  "[[/33#kk%044  UGd+5!#E7+_ _   "  "M:44A%++e:L:Q:Q { **, Mr)&__doc__collections.abcrSrVr8rCtypingrr uint8int8int16int32rZuint16uint32uint64_INTEGRAL_TYPESfloat16bfloat16r_float64_FLOATING_TYPES float8_e4m3fn float8_e5m2float8_e4m3fnuzfloat8_e5m2fnuzr^ complex32 bcomplex32 complex64 complex128r\r#rFrBrrerKrr r}sD   KK JJ KK KK KK LL LL LL ==%..%--O      //5#3#3U__eFVFVW#jj;?;@@@%g g  g  g gg15ggr