tensorflow: Cannot convert a symbolic Tensor (Neg_1:0) to a numpy array

Hello.

I am trying to derive with Tensorflow, and get an error telling me to report the behavior to the TensorFlow team. Hence this post.

My code:

from math import e

def cal_sigma(y):
    return 1 / (1 + np.power(e, -y) )

@tf.function
def get_derivative(x, y):
    with tf.GradientTape(persistent=True) as tape:
        tape.watch(x)
        tape.watch(y)
        y_sigma = cal_sigma(-y)
        f = (x + y_sigma) / np.power((x -y), 2)
    df_dy = tape.gradient(f, y) 
    return df_dy

x = tf.constant (1.)
y = tf.constant (-3.)
print(get_derivative(x, y))

If I derive with respect to x (df_dy = tape.gradient(f, x) in second to last line; it works. However, with y, I get the following error:

WARNING:tensorflow:AutoGraph could not transform <method-wrapper '__call__' of numpy.ufunc object at 0x110a96950> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: Cannot convert a symbolic Tensor (Neg_1:0) to a numpy array.
WARNING: AutoGraph could not transform <method-wrapper '__call__' of numpy.ufunc object at 0x110a96950> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: Cannot convert a symbolic Tensor (Neg_1:0) to a numpy array.
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-11-a4cdc628e4ad> in <module>
      2 y = tf.constant (-3.)
      3 #print(get_derivative(x, y))
----> 4 print(get_derivative2(x, y))

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
    566         xla_context.Exit()
    567     else:
--> 568       result = self._call(*args, **kwds)
    569 
    570     if tracing_count == self._get_tracing_count():

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
    604       # In this case we have not created variables on the first call. So we can
    605       # run the first trace but we should fail if variables are created.
--> 606       results = self._stateful_fn(*args, **kwds)
    607       if self._created_variables:
    608         raise ValueError("Creating variables on a non-first call to a function"

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
   2360     """Calls a graph function specialized to the inputs."""
   2361     with self._lock:
-> 2362       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
   2363     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2364 

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   2701 
   2702       self._function_cache.missed.add(call_context_key)
-> 2703       graph_function = self._create_graph_function(args, kwargs)
   2704       self._function_cache.primary[cache_key] = graph_function
   2705       return graph_function, args, kwargs

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   2591             arg_names=arg_names,
   2592             override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593             capture_by_value=self._capture_by_value),
   2594         self._function_attributes,
   2595         # Tell the ConcreteFunction to clean up its graph once it goes out of

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    976                                           converted_func)
    977 
--> 978       func_outputs = python_func(*func_args, **func_kwargs)
    979 
    980       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    437         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    438         # the function a weak reference to itself to avoid a reference cycle.
--> 439         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    440     weak_wrapped_fn = weakref.ref(wrapped_fn)
    441 

/opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in wrapper(*args, **kwargs)
    966           except Exception as e:  # pylint:disable=broad-except
    967             if hasattr(e, "ag_error_metadata"):
--> 968               raise e.ag_error_metadata.to_exception(e)
    969             else:
    970               raise

NotImplementedError: in converted code:

    <ipython-input-9-af8fccc461fb>:20 get_derivative2  *
        y_sigma = cal_sigma(-y)
    <ipython-input-9-af8fccc461fb>:4 cal_sigma  *
        return 1 / (1 + np.power(e, -y) )
    /opt/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:728 __array__
        " array.".format(self.name))

    NotImplementedError: Cannot convert a symbolic Tensor (Neg_1:0) to a numpy array.