"""A scope that tracks all trainable variable accesses within it.

__slots__ = ["_variable_watcher"]

def __init__(self):

self._variable_watcher = None

def __enter__(self):

self._variable_watcher = pywrap_tfe.TFE_Py_VariableWatcherNew()

return self

def __exit__(self, typ, value, traceback):

pywrap_tfe.TFE_Py_VariableWatcherRemove(self._variable_watcher)

def watched_variables(self):

"""Returns a tuple of variables accessed under this scope."""

return pywrap_tfe.TFE_Py_VariableWatcherWatchedVariables(

"""Stop all gradient recording (backprop and forwardprop)."""

is_stopped = pywrap_tfe.TFE_Py_TapeSetIsStopped()

try:

if not is_stopped:

pywrap_tfe.TFE_Py_TapeSetStopOnThread()

yield

finally:

if not is_stopped:

"""Returns true if any tape in the stack watches any of these tensors.

forward_function=None):

"""Records the operation on all tapes in the stack."""

pywrap_tfe.TFE_Py_TapeSetRecordOperation(op_type, output_tensors,

input_tensors, backward_function,

backward_function):

"""Records the operation on all backward tapes in the stack."""

pywrap_tfe.TFE_Py_TapeSetRecordOperationBackprop(op_type, output_tensors,

input_tensors,

backward_function,

forwardprop_output_indices):

"""Records the operation on all forward accumulators in the stack.

pywrap_tfe.TFE_Py_TapeSetRecordOperationForwardprop(

op_type, output_tensors, input_tensors, backward_function,