git clone https://github.com/ivy-llc/ivy.gitcd ivypip install --user -e.

importivyimporttorchimporttensorflowastfdeftorch_fn(x):a=torch.mul(x,x)b=torch.mean(x)returnx*a+btf_fn=ivy.transpile(torch_fn,source="torch",target="tensorflow")tf_x=tf.convert_to_tensor([1.,2.,3.])ret=tf_fn(tf_x)

importivyimporttorchdeftorch_fn(x):a=torch.mul(x,x)b=torch.mean(x)returnx*a+btorch_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 documentationivy.transpile()# Traces an efficient fully-functional graph from a function, removing all wrapping and redundant code. See usage in the documentationivy.trace_graph()

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

importivyimporttorchimporttensorflowastfdeftorch_fn(x):x=torch.abs(x)returntorch.sum(x)x1=torch.tensor([1.,2.])x1=tf.convert_to_tensor([1.,2.])# Transpilation happens eagerlytf_fn=ivy.transpile(test_fn,source="torch",target="tensorflow")# tf_fn is now tensorflow code and runs efficientlyret=tf_fn(x1)

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

importivyimportkorniaimporttensorflowastfx2=tf.random.normal((5,3,4,4))# Module is provided -> transpilation happens lazilytf_kornia=ivy.transpile(kornia,source="torch",target="tensorflow")# The transpilation is initialized here, and this function is converted to tensorflowret=tf_kornia.color.rgb_to_grayscale(x2)# Transpilation has already occurred, the tensorflow function runs efficientlyret=tf_kornia.color.rgb_to_grayscale(x2)