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)

# 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()

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)

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)