multipers.array_api package

Submodules

multipers.array_api.jax module

multipers.array_api.numpy module

multipers.array_api.numpy._dtype_like(x)

multipers.array_api.numpy.add_at(x, idx, y)

multipers.array_api.numpy.any(x, axis=None, dim=None, **kwargs)

multipers.array_api.numpy.argsort(x, axis=-1)

multipers.array_api.numpy.ascontiguous(x)

multipers.array_api.numpy.astensor(x, contiguous=False, dtype=None)

multipers.array_api.numpy.astype(x, dtype)

multipers.array_api.numpy.cartesian_product(*arrays, dtype=None)

multipers.array_api.numpy.cdist(x, y, p=2)

multipers.array_api.numpy.check_keops()

multipers.array_api.numpy.clip(x, min=None, max=None)

multipers.array_api.numpy.copy(x)

multipers.array_api.numpy.device(x)

multipers.array_api.numpy.div_at(x, idx, y)

multipers.array_api.numpy.dtype_default()

multipers.array_api.numpy.dtype_is_float(dtype)

multipers.array_api.numpy.empty(*args, device=None, **kwargs)

multipers.array_api.numpy.from_numpy(x)

multipers.array_api.numpy.has_grad(_)

multipers.array_api.numpy.inf_value(array)

multipers.array_api.numpy.is_float(x)

multipers.array_api.numpy.is_int(x)

multipers.array_api.numpy.is_promotable(x)

multipers.array_api.numpy.is_tensor(x)

multipers.array_api.numpy.jit(fn=None, **kwargs)

multipers.array_api.numpy.linspace(low, high, r, device=None, dtype=None)

multipers.array_api.numpy.logsumexp(x, axis=None, dim=None, keepdims=False, keepdim=None)

multipers.array_api.numpy.max_at(x, idx, y)

multipers.array_api.numpy.maxvalues(x, **kwargs)

Parameters:

x (ndarray)

multipers.array_api.numpy.mean(x, axis=None, dim=None, **kwargs)

multipers.array_api.numpy.min_at(x, idx, y)

multipers.array_api.numpy.min_k(x, k, axis=-1)

multipers.array_api.numpy.minvalues(x, **kwargs)

Parameters:

x (ndarray)

multipers.array_api.numpy.mul_at(x, idx, y)

multipers.array_api.numpy.norm(x, axis=None, dim=None, **kwargs)

multipers.array_api.numpy.pdist(x, p=2)

multipers.array_api.numpy.quantile_closest(x, q, axis=None)

multipers.array_api.numpy.relu(x)

multipers.array_api.numpy.set_at(x, idx, y)

multipers.array_api.numpy.size(x)

multipers.array_api.numpy.sort(x, axis=-1)

multipers.array_api.numpy.split_with_sizes(arr, sizes)

multipers.array_api.numpy.sum(x, axis=None, dim=None, **kwargs)

multipers.array_api.numpy.to_device(x, device)

multipers.array_api.numpy.top_k(x, k, axis=-1)

multipers.array_api.numpy.unique(x, assume_sorted=False, _mean=False)

multipers.array_api.torch module

multipers.array_api.torch._dtype_like(x)

multipers.array_api.torch.add_at(x, idx, y)

multipers.array_api.torch.any(x, axis=None, dim=None, **kwargs)

multipers.array_api.torch.argsort(x, axis=-1)

multipers.array_api.torch.ascontiguous(x)

multipers.array_api.torch.asnumpy(x, dtype=None)

multipers.array_api.torch.astensor(x, contiguous=False, dtype=None)

multipers.array_api.torch.astype(x, dtype)

multipers.array_api.torch.check_keops()

multipers.array_api.torch.clip(x, min=None, max=None)

multipers.array_api.torch.copy(x)

multipers.array_api.torch.device(x)

multipers.array_api.torch.div_at(x, idx, y)

multipers.array_api.torch.dtype_default()

multipers.array_api.torch.dtype_is_float(dtype)

multipers.array_api.torch.has_grad(x)

multipers.array_api.torch.inf_value(array)

multipers.array_api.torch.is_float(x)

multipers.array_api.torch.is_int(x)

multipers.array_api.torch.is_promotable(x)

multipers.array_api.torch.is_tensor(x)

multipers.array_api.torch.jit(fn=None, **kwargs)

multipers.array_api.torch.logsumexp(x, axis=None, dim=None, keepdims=False, keepdim=None)

multipers.array_api.torch.max_at(x, idx, y)

multipers.array_api.torch.maxvalues(x, axis=None, dim=None, keepdims=False, keepdim=None)

multipers.array_api.torch.mean(x, axis=None, dim=None, **kwargs)

multipers.array_api.torch.min_at(x, idx, y)

multipers.array_api.torch.min_k(x, k, axis=-1)

multipers.array_api.torch.minvalues(x, axis=None, dim=None, keepdims=False, keepdim=None)

multipers.array_api.torch.mul_at(x, idx, y)

multipers.array_api.torch.norm(x, axis=None, dim=None, **kwargs)

multipers.array_api.torch.quantile_closest(x, q, axis=None)

multipers.array_api.torch.set_at(x, idx, y)

multipers.array_api.torch.size(x)

multipers.array_api.torch.sort(x, axis=-1)

multipers.array_api.torch.split_with_sizes(arr, sizes)

multipers.array_api.torch.sum(x, axis=None, dim=None, **kwargs)

multipers.array_api.torch.to_device(x, device)

multipers.array_api.torch.top_k(x, k, axis=-1)

multipers.array_api.torch.unique(x, assume_sorted=False, _mean=False)

Module contents

multipers.array_api._has_jit(api)

multipers.array_api._looks_like_jax(x)

multipers.array_api._looks_like_torch(x)

multipers.array_api._module_name(x)

multipers.array_api.add_interface(interface)

Parameters:

interface (str)

multipers.array_api.api_from_tensor(x, *, verbose=False, strict=False, jit_promote=False)

Parameters:

• verbose (bool)

• jit_promote (bool)

multipers.array_api.api_from_tensors(*args, jit_promote=False)

Parameters:

jit_promote (bool)

multipers.array_api.check_keops()

multipers.array_api.is_jax_api(api)

multipers.array_api.to_numpy(x, dtype=None)