You can also install ivy from source if you want to take advantage of the latest changes:

git clone https://github.com/ivy-llc/ivy.git
cd ivy
pip install --user -e .

import ivy
import torch
import tensorflow as tf

def torch_fn(x):
    a = torch.mul(x, x)
    b = torch.mean(x)
    return x * a + b

tf_fn = ivy.transpile(torch_fn, source="torch", target="tensorflow")

tf_x = tf.convert_to_tensor([1., 2., 3.])
ret = tf_fn(tf_x)

import ivy
import torch

def torch_fn(x):
    a = torch.mul(x, x)
    b = torch.mean(x)
    return x * a + b

torch_x = torch.tensor([1., 2., 3.])
graph = ivy.trace_graph(jax_fn, to="torch", args=(torch_x,))
ret = graph(torch_x)

ivy's transpiler allows you to use code from any other framework in your own code. Feel free to head over to the docs for the full API reference, but the functions you'd most likely want to use are:

# Converts framework-specific code to a target framework of choice. See usage in the documentation
ivy.transpile()

# Traces an efficient fully-functional graph from a function, removing all wrapping and redundant code. See usage in the documentation
ivy.trace_graph()

ivy.transpile will eagerly transpile if a class or function is provided

import ivy
import torch
import tensorflow as tf

def torch_fn(x):
    x = torch.abs(x)
    return torch.sum(x)

x1 = torch.tensor([1., 2.])
x1 = tf.convert_to_tensor([1., 2.])

# Transpilation happens eagerly
tf_fn = ivy.transpile(test_fn, source="torch", target="tensorflow")

# tf_fn is now tensorflow code and runs efficiently
ret = tf_fn(x1)

ivy.transpile will lazily transpile if a module (library) is provided

import ivy
import kornia
import tensorflow as tf

x2 = tf.random.normal((5, 3, 4, 4))

# Module is provided -> transpilation happens lazily
tf_kornia = ivy.transpile(kornia, source="torch", target="tensorflow")

# The transpilation is initialized here, and this function is converted to tensorflow
ret = tf_kornia.color.rgb_to_grayscale(x2)

# Transpilation has already occurred, the tensorflow function runs efficiently
ret = tf_kornia.color.rgb_to_grayscale(x2)