Class ndarray

torch/_numpy/_ndarray.py:174–465

class ndarray:
    def __init__(self, t=None):
        if t is None:
            self.tensor = torch.Tensor()
        elif isinstance(t, torch.Tensor):
            self.tensor = t
        else:
            raise ValueError(
                "ndarray constructor is not recommended; prefer"
                "either array(...) or zeros/empty(...)"
            )

    # Register NumPy functions as methods
    for method, name in methods.items():
        fn = getattr(_funcs, name or method)
        vars()[method] = create_method(fn, method)

    # Regular methods but coming from ufuncs
    conj = create_method(_ufuncs.conjugate, "conj")
    conjugate = create_method(_ufuncs.conjugate)

    for method, name in dunder.items():
        fn = getattr(_ufuncs, name or method)
        method = f"__{method}__"
        vars()[method] = create_method(fn, method)

    for method, name in ri_dunder.items():
        fn = getattr(_ufuncs, name or method)
        plain = f"__{method}__"
        vars()[plain] = create_method(fn, plain)
        rvar = f"__r{method}__"
        vars()[rvar] = create_method(lambda self, other, fn=fn: fn(other, self), rvar)
        ivar = f"__i{method}__"
        vars()[ivar] = create_method(
            lambda self, other, fn=fn: fn(self, other, out=self), ivar
        )

    # There's no __idivmod__
    __divmod__ = create_method(_ufuncs.divmod, "__divmod__")
    __rdivmod__ = create_method(
        lambda self, other: _ufuncs.divmod(other, self), "__rdivmod__"
    )

    # prevent loop variables leaking into the ndarray class namespace
    del ivar, rvar, name, plain, fn, method

    @property
    def shape(self):
        return tuple(self.tensor.shape)

    @property
    def size(self):
        return self.tensor.numel()

    @property
    def ndim(self):
        return self.tensor.ndim