import numpy as np # or torch, tensorflow, JAX ...
from einop import einop

a = np.random.uniform(size=(10, 20))
b = np.random.uniform(size=(20, 15))

y = einop(a, b, "a b, b c -> a c") # matrix multiplication

assert y.shape == (10, 15)

import numpy as np
from einop import einop

a = np.random.uniform(size=(10, 15))
b = np.random.uniform(size=(20, 15))
c = np.random.uniform(size=(30, 15))

y = einop((a, b, c) "* a") # concatenate along axis 0

assert y.shape == (10 + 20 + 30, 15)

x = np.random.randn(100, 5, 3)

einop(x, 'i j k -> k i j').shape
>>> (3, 100, 5)

x = np.random.randn(100, 5, 3)

einop(x, 'i j k -> i j', reduction='sum').shape
>>> (100, 5)

x = np.random.randn(100, 5, 3)

einop(x, 'i j k -> i j k l', l=10).shape
>>> (100, 5, 3, 10)