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Defined in tensorflow/python/eager/

Wraps the TF 1.x function fn into a graph function.

The python function fn will be called once with symbolic arguments specified in the signature, traced, and turned into a graph function. Any variables created by fn will be owned by the object returned by wrap_function. The resulting graph function can be called with tensors which match the signature.

def f(x, do_add):
  v = tf.Variable(5.0)
  if do_add:
    op = v.assign_add(x)
    op = v.assign_sub(x)
  with tf.control_dependencies([op]):
    return v.read_value()

f_add = tf.compat.v1.wrap_function(f, [tf.TensorSpec((), tf.float32), True])

assert float(f_add(1.0)) == 6.0
assert float(f_add(1.0)) == 7.0

# Can call tf.compat.v1.wrap_function again to get a new trace, a new set
# of variables, and possibly different non-template arguments.
f_sub= tf.compat.v1.wrap_function(f, [tf.TensorSpec((), tf.float32), False])

assert float(f_sub(1.0)) == 4.0
assert float(f_sub(1.0)) == 3.0

Both tf.compat.v1.wrap_function and tf.function create a callable TensorFlow graph. But while tf.function runs all stateful operations (e.g. tf.print) and sequences operations to provide the same semantics as eager execution, wrap_function is closer to the behavior of in TensorFlow 1.x. It will not run any operations unless they are required to compute the function's outputs, either through a data dependency or a control dependency. Nor will it sequence operations.

Unlike tf.function, wrap_function will only trace the Python function once. As with placeholders in TF 1.x, shapes and dtypes must be provided to wrap_function's signature argument.

Since it is only traced once, variables and state may be created inside the function and owned by the function wrapper object.


  • fn: python function to be wrapped
  • signature: the placeholder and python arguments to be passed to the wrapped function
  • name: Optional. The name of the function.


the wrapped graph function.