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Getting Started

Installation
Quickstart Guide

Backends

Backends overview
Feature support matrix
JAX backend (default)
NumPy backend
CuPy backend
PyTorch backend
Dask backend
ndonnx backend

Physical Units

Quantity
Math Operations
Standard Units
Constants
Unit Conversion
Temperature Conversions

Unit-aware Operations

Array Creation
NumPy Functions
Einstein Operations
Linear Algebra Functions
Fourier Transform Functions
JAX LAX Functions
Activation Functions
Checking Function Units
Assigning Function Units
JAX Transforms: JIT and vmap with Units
Automatic Differentiation with Units
Sparse Matrices with Units
Matplotlib Integration
Unit-Aware Type Annotations

Advanced Tutorials

Combining and Defining Unit
Mechanism of Quantity
Unit-aware Computation with CustomArray
FAQs

API Documentation

Release Notes
saiunit module
saiunit.typing module
saiunit.autograd module
Mathematical Functions
saiunit.linalg module
- dot
- multi_dot
- vdot
- vecdot
- inner
- kron
- matmul
- tensordot
- matrix_power
- cross
- cholesky
- outer
- det
- solve
- tensorsolve
- lstsq
- inv
- pinv
- tensorinv
- qr
- svd
- svdvals
- eig
- eigh
- eigvals
- eigvalsh
- norm
- matrix_norm
- vector_norm
- trace
- diagonal
- matrix_transpose
- cond
- matrix_rank
- slogdet
saiunit.lax module
- acos
- acosh
- asin
- asinh
- atan
- atanh
- collapse
- cumlogsumexp
- bessel_i0e
- bessel_i1e
- digamma
- lgamma
- erf
- erfc
- erf_inv
- logistic
- atan2
- polygamma
- igamma
- igammac
- igamma_grad_a
- random_gamma_grad
- zeta
- betainc
- shift_left
- shift_right_arithmetic
- shift_right_logical
- fft
- zeros_like_array
- iota
- broadcasted_iota
- rsqrt
- div
- pow
- integer_pow
- mul
- rem
- batch_matmul
- conv
- conv_transpose
- dot_general
- slice
- dynamic_slice
- dynamic_update_slice
- gather
- index_take
- slice_in_dim
- index_in_dim
- dynamic_slice_ind_dim
- dynamic_index_in_dim
- dynamic_update_slice_in_dim
- dynamic_update_index_in_dim
- sort
- sort_key_val
- neg
- cummax
- cummin
- cumsum
- scatter
- scatter_add
- scatter_sub
- scatter_mul
- scatter_min
- scatter_max
- scatter_apply
- sub
- complex
- pad
- clamp
- convert_element_type
- bitcast_convert_type
- approx_max_k
- approx_min_k
- top_k
- broadcast
- broadcast_in_dim
- broadcast_to_rank
- population_count
- clz
- eq
- ne
- ge
- gt
- le
- lt
- cholesky
- eig
- eigh
- hessenberg
- lu
- qdwh
- qr
- schur
- svd
- tridiagonal
- householder_product
- triangular_solve
- tridiagonal_solve
- reduce
- reduce_precision
- broadcast_shapes
saiunit.fft module
- fft
- rfft
- ifft
- irfft
- fft2
- fftn
- rfft2
- rfftn
- ifft2
- ifftn
- irfft2
- irfftn
- fftfreq
- rfftfreq
- fftshift
- ifftshift
saiunit.sparse module
saiunit.constants module

.rst

saiunit.math.finfo

saiunit.math.finfo#

saiunit.math.finfo(a)#

Return type:: finfo

Contents

finfo()

By BrainX Ecosystem

Last updated on Aug 11, 2026.