import eagerpy as ep

import torch
x = torch.tensor([1., 2., 3.])
x = ep.PyTorchTensor(x)

import tensorflow as tf
x = tf.constant([1., 2., 3.])
x = ep.TensorFlowTensor(x)

import jax.numpy as np
x = np.array([1., 2., 3.])
x = ep.JAXTensor(x)

import numpy as np
x = np.array([1., 2., 3.])
x = ep.NumPyTensor(x)

# In all cases, the resulting EagerPy tensor provides the same
# interface. This makes it possible to write code that works natively
# independent of the underlying framework.

# EagerPy tensors provide a lot of functionality through methods, e.g.
x.sum()
x.sqrt()
x.clip(0, 1)

# but EagerPy also provides them as functions, e.g.
ep.sum(x)
ep.sqrt(x)
ep.clip(x, 0, 1)
ep.uniform(x, (3, 3), low=-1., high=1.)  # x is needed to infer the framework