creation

Construct a new `Tensor` from Python data, a NumPy array, or another Tensor.

Convert data to a tensor, avoiding a copy when the source already matches.

Create a CPU tensor from a NumPy `ndarray` with shared storage.

Return a tensor filled with the additive identity element, zero.

Return a tensor filled with the multiplicative identity element, one.

Return a tensor whose elements are uninitialised (undefined memory).

Return a tensor filled with a constant scalar value.

Return a 2-D matrix with ones on the main diagonal and zeros elsewhere.

Return a 1-D tensor of evenly spaced values over a half-open interval.

Return a 1-D tensor of `steps` equally spaced values over a closed interval.

Return a zero-filled tensor with the same shape, dtype, and device as `t`.

Return an all-ones tensor with the same shape, dtype, and device as `t`.

Return an uninitialised tensor with the same shape, dtype, and device as `t`.

Return a constant-filled tensor with the same shape, dtype, and device as `t`.

Return a 1-D tensor of `steps` values evenly spaced on a logarithmic scale.

Set the seed of the default Philox counter-based random number generator.

Return a tensor of samples drawn from the continuous uniform distribution.

Return a tensor of samples drawn from the standard normal distribution.

Return a tensor of samples drawn from the discrete uniform distribution.

Return a tensor of independent Bernoulli trials with success probability `p`.

Return a tensor of samples drawn from a parametric normal distribution.

Return a $U[0,1)$ random tensor with the same shape, dtype, and device as `t`.

Return a $\mathcal{N}(0,1)$ random tensor with the same shape, dtype, and device as `t`.