def testGradientFunction(self):
   # Input to tf.py_func is necessary, otherwise get_gradient_function()
   # returns None per default.
   a = constant_op.constant(0)
   x, = script_ops.py_func(lambda a: 0, [a], [dtypes.int64])
   y, = script_ops.py_func(lambda a: 0, [a], [dtypes.int64], stateful=False)
   self.assertEqual(None, ops.get_gradient_function(x.op))
   self.assertEqual(None, ops.get_gradient_function(y.op))

 def testOverrideGradients(self):
   g = ops.Graph()
   x = an_op(g)
   with g.gradient_override_map({"copy": "copy_override"}):
     y = copy_op(x)
   fn = ops.get_gradient_function(y.op)
   self.assertEqual(_CopyOverrideGrad, fn)

 def testNonExistentOverride(self):
   g = ops.Graph()
   x = an_op(g)
   with g.gradient_override_map({"copy": "unknown_override"}):
     y = copy_op(x)
   with self.assertRaisesRegexp(LookupError, "unknown_override"):
     fn = ops.get_gradient_function(y.op)

def find_non_differentiable(inputs, outputs):
    """
    Searches through a TensorFlow graph to find non-differentiable elements
    between ``inputs`` and ``outputs`` (elements that would prevent us from
    computing ``d_outputs / d_inputs``.

    Parameters
    ----------
    inputs : list of ``tf.Tensor``
        Input tensors
    outputs : list of ``tf.Tensor``
        Output tensors
    """

    for o in outputs:
        if o in inputs:
            continue
        else:
            try:
                grad = get_gradient_function(o.op)

                if grad is None and len(o.op.inputs) > 0:
                    # note: technically we're not sure that this op is
                    # on the path to inputs. we could wait and propagate this
                    # until we find inputs, but that can take a long time for
                    # large graphs. it seems more useful to fail quickly, and
                    # risk some false positives
                    raise LookupError
                find_non_differentiable(inputs, o.op.inputs)
            except LookupError:
                raise SimulationError(
                    "Graph contains non-differentiable "
                    "elements: %s" % o.op)

 def _Gradient(tensors, devices):
   reduce_tensors, _ = nccl_reduce(tensors, devices)
   tensor_ops = [t.op for t in reduce_tensors]
   d_tensors = _DeviceTensors(tensors, devices)
   grad_tensors = [
       ops.get_gradient_function(op)(op, loss)
       for op, loss in zip(tensor_ops, d_tensors)
   ]
   return grad_tensors, []

#.........这里部分代码省略.........
    # Add the ops in 'to_ops' into the queue.
    to_ops_set = set()
    for op in to_ops:
      # 'ready' handles the case where one output gradient relies on
      # another output's gradient.
      # pylint: disable=protected-access
      ready = (pending_count[op._id] == 0)
      if ready and op._id not in to_ops_set:
        to_ops_set.add(op._id)
        queue.append(op)
      # pylint: enable=protected-access

    if loop_state:
      loop_exits = loop_state.ProcessUnusedLoopExits(pending_count, to_ops_set)
      for y in loop_exits:
        if _IsTrainable(y):
          _SetGrad(grads, y, loop_state.ZerosLikeForExit(y))
          queue.append(y.op)

    # The set of 'from_ops'.
    stop_ops = _StopOps(from_ops, pending_count)
    while queue:
      # generate gradient subgraph for op.
      op = queue.popleft()
      with _maybe_colocate_with(op, colocate_gradients_with_ops):
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=True)
        out_grads = _AggregatedGrads(grads, op, loop_state, aggregation_method)
        if loop_state:
          loop_state.ExitGradWhileContext(op, before=True)

        grad_fn = None
        # pylint: disable=protected-access
        is_func_call = ops.get_default_graph()._is_function(op.type)
        has_out_grads = any(isinstance(g, ops.Tensor) or g for g in out_grads)
        if has_out_grads and (op._id not in stop_ops):
          if is_func_call:
            grad_fn = ops.get_default_graph()._get_function(
                op.type).python_grad_func
            # pylint: enable=protected-access
          else:
            # A grad_fn must be defined, either as a function or as None
            # for ops that do not have gradients.
            try:
              grad_fn = ops.get_gradient_function(op)
            except LookupError:
              raise LookupError(
                  "No gradient defined for operation '%s' (op type: %s)" %
                  (op.name, op.type))
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=False)
        if (grad_fn or is_func_call) and has_out_grads:
          # NOTE: If _AggregatedGrads didn't compute a value for the i'th
          # output, it means that the cost does not depend on output[i],
          # therefore dC/doutput[i] is 0.
          for i, out_grad in enumerate(out_grads):
            if (not isinstance(out_grad, ops.Tensor) and
                not out_grad) and _IsTrainable(op.outputs[i]):
              # Only floating-point outputs get a zero gradient. Gradient
              # functions should ignore the gradient for other outputs.
              if loop_state:
                out_grads[i] = loop_state.ZerosLike(op, i)
              else:
                out_grads[i] = control_flow_ops.ZerosLikeOutsideLoop(op, i)
          with ops.name_scope(op.name + "_grad"):
            # pylint: disable=protected-access
            with ops.get_default_graph()._original_op(op):
              # pylint: enable=protected-access
              if grad_fn:
                # If grad_fn was found, do not use SymbolicGradient even for
                # functions.
                in_grads = grad_fn(op, *out_grads)
              else:
                # For function call ops, we add a 'SymbolicGradient'
                # node to the graph to compute gradients.
                in_grads = _SymGrad(op, out_grads)
              in_grads = _AsList(in_grads)
              _VerifyGeneratedGradients(in_grads, op)
              if gate_gradients and len(
                  [x for x in in_grads if x is not None]) > 1:
                in_grads = control_flow_ops.tuple(in_grads)
          _LogOpGradients(op, out_grads, in_grads)
        else:
          # If no grad_fn is defined or none of out_grads is available,
          # just propagate a list of None backwards.
          in_grads = [None] * len(op.inputs)
        for t_in, in_grad in zip(op.inputs, in_grads):
          if in_grad is not None:
            if isinstance(in_grad, ops.Tensor):
              in_grad.set_shape(t_in.get_shape())
            _SetGrad(grads, t_in, in_grad)
        if loop_state:
          loop_state.ExitGradWhileContext(op, before=False)

      # Update pending count for the inputs of op and enqueue ready ops.
      _UpdatePendingAndEnqueueReady(grads, op, queue, pending_count, loop_state)

  if loop_state:
    loop_state.PostProcessing()
  return [_GetGrad(grads, x) for x in xs]

 def testRegisterGradients(self):
   g = ops.Graph()
   x = an_op(g)
   y = copy_op(x)
   fn = ops.get_gradient_function(y.op)
   self.assertEqual(_CopyGrad, fn)

#.........这里部分代码省略.........
    to_ops = [t.op for t in ys]
    from_ops = [t.op for t in xs]
    pending_count, has_control_flow = _PendingCount(ops.get_default_graph(),
                                                    to_ops, from_ops)

    # Iterate over the collected ops.
    #
    # grads: op => list of gradients received on each output endpoint of the
    # op.  The gradients for each endpoint are initially collected as a list.
    # When it is time to call the op's gradient function, for each endpoint we
    # aggregate the list of received gradients into a Add() Operation if there
    # is more than one.
    grads = {}

    # Add the initial gradients for the ys.
    for y, grad_y in zip(ys, grad_ys):
      _SetGrad(grads, y, grad_y)

    # Initialize queue with to_ops.
    queue = collections.deque()
    # Add the ops in 'to_ops' into the queue.
    to_ops_set = set()
    for op in to_ops:
      # 'ready' handles the case where one output gradient relies on
      # another output's gradient.
      ready = (pending_count[op._id] == 0)
      if ready and op._id not in to_ops_set:  # pylint: disable=protected-access
        to_ops_set.add(op._id)
        queue.append(op)
    # The set of 'from_ops'.
    stop_ops = _StopOps(from_ops, pending_count)
    while queue:
      # generate gradient subgraph for op.
      op = queue.popleft()
      with ops.device(_GetGradsDevice(op, colocate_gradients_with_ops)):
        if has_control_flow:
          control_flow_ops.EnterGradWhileContext(op)
        out_grads = _AggregatedGrads(grads, op, has_control_flow,
                                     aggregation_method)
        grad_fn = None
        if any(out_grads) and op._id not in stop_ops:
          # A grad_fn must be defined, either as a function or as None
          # for ops that do not have gradients.
          try:
            grad_fn = ops.get_gradient_function(op)
          except LookupError:
            raise LookupError(
                "No gradient defined for operation '%s' (op type: %s)" %
                (op.name, op.type))
        if grad_fn and any(out_grads):
          # NOTE: If _AggregatedGrads didn't compute a value for the i'th
          # output, it means that the cost does not depend on output[i],
          # therefore dC/doutput[i] is 0.
          for i, out_grad in enumerate(out_grads):
            if (not out_grad and
                dtypes.as_dtype(op.outputs[i].dtype).base_dtype in
                (dtypes.float32, dtypes.float64)):
              # Only floating-point outputs get a zero gradient. Gradient
              # functions should ignore the gradient for other outputs.
              out_grads[i] = array_ops.zeros_like(op.outputs[i])
          with ops.name_scope(op.name + "_grad"):
            # pylint: disable=protected-access
            with ops.get_default_graph()._original_op(op):
              # pylint: enable=protected-access
              op_wrapper = op
              if has_control_flow:
                op_wrapper = control_flow_ops.MakeWrapper(op)
              in_grads = _AsList(grad_fn(op_wrapper, *out_grads))
              _VerifyGeneratedGradients(in_grads, op)
              if gate_gradients and len(in_grads) > 1:
                in_grads = control_flow_ops.tuple(in_grads)
          logging.vlog(1, "Gradient for '" + op.name + "'")
          logging.vlog(1, "  in  --> %s",
                       ", ".join([x.name for x in out_grads if x]))
          logging.vlog(1, "  out --> %s",
                       ", ".join([x.name for x in in_grads if x]))
        else:
          # If no grad_fn is defined or none of out_grads is available,
          # just propagates a list of None backwards.
          in_grads = [None] * len(op.inputs)
        for t_in, in_grad in zip(op.inputs, in_grads):
          if in_grad:
            _SetGrad(grads, t_in, in_grad)
        if has_control_flow:
          control_flow_ops.ExitGradWhileContext(op)

      # update pending count for the inputs of op.
      for x in op.inputs:
        pending_count[x.op._id] -= 1
        ready = (pending_count[x.op._id] == 0)
        if has_control_flow and not ready:
          ready = (pending_count[x.op._id] > 0 and
                   control_flow_ops.IsLoopSwitch(x.op))
        if ready:
          queue.append(x.op)
      for x in op.control_inputs:
        pending_count[x._id] -= 1
        if pending_count[x._id] is 0:
          queue.append(x)
  return [_GetGrad(grads, x) for x in xs]

#.........这里部分代码省略.........
      if ready and op not in to_ops_set and op in reachable_to_ops:
        to_ops_set.add(op)
        queue.append(op)

    if loop_state:
      loop_exits = loop_state.ProcessUnusedLoopExits(pending_count, to_ops_set)
      for y in loop_exits:
        if IsTrainable(y):
          _SetGrad(grads, y, loop_state.ZerosLikeForExit(y))
          queue.append(y.op)

    stop_ops = _StopOps(from_ops, stop_gradient_ops, pending_count, xs)
    while queue:
      # generate gradient subgraph for op.
      op = queue.popleft()
      with _maybe_colocate_with(op, gradient_uid, colocate_gradients_with_ops):
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=True)
        out_grads = _AggregatedGrads(grads, op, gradient_uid, loop_state,
                                     aggregation_method)
        if loop_state:
          loop_state.ExitGradWhileContext(op, before=True)

        grad_fn = None
        func_call = None
        is_partitioned_call = _IsPartitionedCall(op)
        # pylint: disable=protected-access
        is_func_call = (
            src_graph._is_function(op.type) or is_partitioned_call)
        # pylint: enable=protected-access
        has_out_grads = any(isinstance(g, ops.Tensor) or g for g in out_grads)
        if has_out_grads and (op not in stop_ops):
          try:
            grad_fn = ops.get_gradient_function(op)
          except LookupError:
            if is_func_call:
              if is_partitioned_call:
                func_call = src_graph._get_function(  # pylint: disable=protected-access
                    compat.as_bytes(op.get_attr("f").name))
              else:
                func_call = src_graph._get_function(op.type)  # pylint: disable=protected-access
              # Note that __defun is not set if the graph is
              # imported. If it's set, we prefer to access the original
              # defun.
              func_call = getattr(op, "__defun", func_call)
              grad_fn = func_call.python_grad_func
            else:
              raise LookupError(
                  "No gradient defined for operation '%s' (op type: %s)" %
                  (op.name, op.type))
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=False)

        # NOTE(skyewm): We don't support computing gradients wrt a loop variable
        # unless it's within the context of a single iteration (i.e. the
        # gradient is wrt to the loop parameter in the body function, not wrt or
        # through the initial value). This means if we're in a while loop
        # context, we should never see a switch node from this context.
        # pylint: disable=protected-access
        if (control_flow_util.IsSwitch(op) and
            op._control_flow_context is not None and
            op._control_flow_context.IsWhileContext() and
            op._control_flow_context ==
            ops.get_default_graph()._get_control_flow_context()):
          _RaiseNoGradWrtInitialLoopValError(op, from_ops, xs)
        # pylint: enable=protected-access

#.........这里部分代码省略.........

    if loop_state:
      loop_exits = loop_state.ProcessUnusedLoopExits(pending_count, to_ops_set)
      for y in loop_exits:
        if _IsTrainable(y):
          _SetGrad(grads, y, loop_state.ZerosLikeForExit(y))
          queue.append(y.op)

    stop_ops = _StopOps(from_ops, stop_gradient_ops, pending_count)
    while queue:
      # generate gradient subgraph for op.
      op = queue.popleft()
      with _maybe_colocate_with(op, colocate_gradients_with_ops):
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=True)
        out_grads = _AggregatedGrads(grads, op, loop_state, aggregation_method)
        if loop_state:
          loop_state.ExitGradWhileContext(op, before=True)

        grad_fn = None
        # pylint: disable=protected-access
        func_call = None
        is_func_call = ops.get_default_graph()._is_function(op.type)
        has_out_grads = any(isinstance(g, ops.Tensor) or g for g in out_grads)
        if has_out_grads and (op._id not in stop_ops):
          if is_func_call:
            func_call = ops.get_default_graph()._get_function(op.type)
            grad_fn = func_call.python_grad_func
            # pylint: enable=protected-access
          else:
            # A grad_fn must be defined, either as a function or as None
            # for ops that do not have gradients.
            try:
              grad_fn = ops.get_gradient_function(op)
            except LookupError:
              raise LookupError(
                  "No gradient defined for operation '%s' (op type: %s)" %
                  (op.name, op.type))
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=False)
        if (grad_fn or is_func_call) and has_out_grads:
          # NOTE: If _AggregatedGrads didn't compute a value for the i'th
          # output, it means that the cost does not depend on output[i],
          # therefore dC/doutput[i] is 0.
          for i, out_grad in enumerate(out_grads):
            if (not isinstance(out_grad, ops.Tensor) and not out_grad) and (
                (not grad_fn and is_func_call) or _IsTrainable(op.outputs[i])):
              # Only trainable outputs or outputs for a function call that
              # will use SymbolicGradient get a zero gradient. Gradient
              # functions should ignore the gradient for other outputs.
              # TODO(apassos) gradients of resource handles might be an
              # issue here because of zeros.
              if loop_state:
                out_grads[i] = loop_state.ZerosLike(op, i)
              else:
                out_grads[i] = control_flow_ops.ZerosLikeOutsideLoop(op, i)
          with ops.name_scope(op.name + "_grad"):
            # pylint: disable=protected-access
            with ops.get_default_graph()._original_op(op):
              # pylint: enable=protected-access
              if grad_fn:
                # If grad_fn was found, do not use SymbolicGradient even for
                # functions.
                in_grads = _MaybeCompile(grad_scope, op, func_call,
                                         lambda: grad_fn(op, *out_grads))
              else:

#.........这里部分代码省略.........
    if self._return_as_is or op_type in _PASS_THROUGH_OPS:
      return self._wrap(super(ImperativeGraph, self).create_op(*args, **kwargs))

    if not output_dtypes:
      return self._wrap(
          super(ImperativeGraph, self).create_op(*args, **kwargs))

    output_has_ref = any([dtype._is_ref_dtype for dtype in output_dtypes])  # pylint: disable=protected-access

    if output_has_ref:
      if op_type not in _REF_OPS_WHITELIST:
        raise errors.UnimplementedError(None, None,
                                        op_type + ' op not supported in '
                                        'imperative graph')

      ret = super(ImperativeGraph, self).create_op(*args, **kwargs)

      if self._in_variable_creation:
        if op_type == 'Assign':
          self.add_pending_init(ret)

      return self._wrap(ret)

    with self.return_as_is():
      # Declares the variables to hold the output values of this op.
      op_output_var = [state_ops.variable_op_v2(
          tensor_shape.TensorShape(None), dtype, container=self._name)
                       for dtype in output_dtypes]
      # Ops to free the resources used by the temporary cache variables.
      # The following two ops are created for each cache variable,
      # having no control dependencies on any other ops :
      # var_handle_op ----> destroy_resource_op
      for dtype, v in zip(output_dtypes, op_output_var):
        with ops.control_dependencies(None):
          self._variable_cleanup_ops += [
              gen_resource_variable_ops.destroy_resource_op(
                  gen_resource_variable_ops.var_handle_op(
                      dtype, tensor_shape.TensorShape(None),
                      container=self._name, shared_name=v.op.name),
                  ignore_lookup_error=True)]

      # Create the conditional to run the original op only when the variable
      # corresponding to the first output is not initialized.
      inited = state_ops.is_variable_initialized(op_output_var[0])
      v_f, v_t = control_flow_ops.ref_switch(op_output_var[0], inited)
      # pylint: disable=protected-access
      v_f_op = gen_array_ops._ref_identity(v_f)
      v_t_op = gen_array_ops._ref_identity(v_t)
      # pylint: enable=protected-access

      with ops.control_dependencies([v_f_op.op]):
        # Create the original op
        orig_op = self._wrap(
            super(ImperativeGraph, self).create_op(*args, **kwargs))
      shapes = [val.get_shape() for val in orig_op.outputs]

      controls = []
      for var, val in zip(op_output_var, orig_op.outputs):
        if (not val.get_shape().is_fully_defined() or
            val.get_shape().num_elements() > 0):
          assign_op = state_ops.assign(var, val, validate_shape=False)
          assign_op.set_shape(val.get_shape())
          controls.append(assign_op)

      values = []
      if len(controls) > 1:
        if control_flow_ops.IsSwitch(orig_op):
          # pylint: disable=protected-access
          controls = gen_control_flow_ops._ref_merge(controls)
          # pylint: enable=protected-access
        else:
          controls = control_flow_ops.tuple(controls)

      for var, val in zip(op_output_var, orig_op.outputs):
        with ops.control_dependencies(controls):
          with self.colocate_with(v_f_op):
            real_val = array_ops.identity(val)
        with ops.control_dependencies([v_t_op.op]):
          with self.colocate_with(v_t_op):
            stored_val = array_ops.identity(var)
          stored_val.set_shape(val.get_shape())
          real_val, _ = control_flow_ops.merge([real_val, stored_val])
        real_val.op.node_def.attr['_gradient_op_type'].CopyFrom(
            attr_value_pb2.AttrValue(s=compat.as_bytes(self._merge_op_type)))
        values.append(real_val)

      for i, _ in enumerate(shapes):
        values[i].set_shape(shapes[i])
      self._outputs_map[orig_op.name] = values
      try:
        self._gradient_function_map[orig_op.name] = ops.get_gradient_function(
            orig_op)
      except (KeyError, LookupError):
        pass
      else:
        orig_op.node_def.attr['_gradient_op_type'].CopyFrom(
            attr_value_pb2.AttrValue(
                s=compat.as_bytes(self._imperative_op_type)))

      return MultiOutputOperation(values, orig_op)

#.........这里部分代码省略.........
  derivatives using a different initial gradient for each y (e.g., if
  one wanted to weight the gradient differently for each value in
  each y).

  Args:
    ys: A `Tensor` or list of tensors to be differentiated.
    xs: A `Tensor` or list of tensors to be used for differentiation.
    grad_ys: Optional. A `Tensor` or list of tensors the same size as
      `ys` and holding the gradients computed for each y in `ys`.
    name: Optional name to use for grouping all the gradient ops together.
      defaults to 'gradients'.
    colocate_gradients_with_ops: If True, try colocating gradients with
      the corresponding op.
    gate_gradients: If True, add a tuple around the gradients returned
      for an operations.  This avoids some race conditions.
    aggregation_method: Specifies the method used to combine gradient terms.
      Accepted values are constants defined in the class `AggregationMethod`.

  Returns:
    A list of `sum(dy/dx)` for each x in `xs`.

  Raises:
    LookupError: if one of the operations between `x` and `y` does not
      have a registered gradient function.
    ValueError: if the arguments are invalid.

  """
  ys = _AsList(ys)
  xs = _AsList(xs)
  if grad_ys is None:
    grad_ys = [None] * len(ys)
  else:
    grad_ys = _AsList(grad_ys)
  with ops.op_scope(ys + xs + grad_ys, name, "gradients"):
    ys = ops.convert_n_to_tensor_or_indexed_slices(ys, name="y")
    xs = ops.convert_n_to_tensor_or_indexed_slices(xs, name="x")
    grad_ys = _DefaultGradYs(grad_ys, ys, colocate_gradients_with_ops)

    # The approach we take here is as follows: Create a list of all ops in the
    # subgraph between the ys and xs.  Visit these ops in reverse order of ids
    # to ensure that when we visit an op the gradients w.r.t its outputs have
    # been collected.  Then aggregate these gradients if needed, call the op's
    # gradient function, and add the generated gradients to the gradients for
    # its input.

    # Initialize the pending count for ops in the connected subgraph from ys
    # to the xs.
    to_ops = [t.op for t in ys]
    from_ops = [t.op for t in xs]
    pending_count, has_control_flow = _PendingCount(
        ops.get_default_graph(), to_ops, from_ops)

    # Iterate over the collected ops.
    #
    # grads: op => list of gradients received on each output endpoint of the
    # op.  The gradients for each endpoint are initially collected as a list.
    # When it is time to call the op's gradient function, for each endpoint we
    # aggregate the list of received gradients into a Add() Operation if there
    # is more than one.
    grads = {}

    # Add the initial gradients for the ys.
    for y, grad_y in zip(ys, grad_ys):
      _SetGrad(grads, y, grad_y)

    # Initialize queue with to_ops.
    queue = collections.deque()
    # Add the ops in 'to_ops' into the queue.
    to_ops_set = set()
    for op in to_ops:
      if op._id not in to_ops_set:
        to_ops_set.add(op._id)
        queue.append(op)
    # The set of 'from_ops'.
    stop_ops = _StopOps(from_ops, pending_count)
    while queue:
      # generate gradient subgraph for op.
      op = queue.popleft()
      with ops.device(_GetGradsDevice(op, colocate_gradients_with_ops)):
        if has_control_flow:
          control_flow_ops.EnterGradWhileContext(op)
        out_grads = _AggregatedGrads(grads, op, has_control_flow,
                                     aggregation_method)
        grad_fn = None
        if any(out_grads) and op._id not in stop_ops:
          # A grad_fn must be defined, either as a function or as None
          # for ops that do not have gradients.
          try:
            grad_fn = ops.get_gradient_function(op)
          except LookupError:
            raise LookupError(
                "No gradient defined for operation '%s' (op type: %s)" %
                (op.name, op.type))
        if grad_fn and any(out_grads):
          # NOTE: If _AggregatedGrads didn't compute a value for the i'th
          # output, it means that the cost does not depend on output[i],
          # therefore dC/doutput[i] is 0.
          for i, out_grad in enumerate(out_grads):
            if (not out_grad
                and types.as_dtype(op.outputs[i].dtype).base_dtype in 

#.........这里部分代码省略.........

    if loop_state:
      loop_exits = loop_state.ProcessUnusedLoopExits(pending_count, to_ops_set)
      for y in loop_exits:
        if _IsTrainable(y):
          _SetGrad(grads, y, loop_state.ZerosLikeForExit(y))
          queue.append(y.op)

    stop_ops = _StopOps(from_ops, stop_gradient_ops, pending_count)
    while queue:
      # generate gradient subgraph for op.
      op = queue.popleft()
      with _maybe_colocate_with(op, colocate_gradients_with_ops):
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=True)
        out_grads = _AggregatedGrads(grads, op, loop_state, aggregation_method)
        if loop_state:
          loop_state.ExitGradWhileContext(op, before=True)

        grad_fn = None
        # pylint: disable=protected-access
        func_call = None
        is_func_call = ops.get_default_graph()._is_function(op.type)
        has_out_grads = any(isinstance(g, ops.Tensor) or g for g in out_grads)
        if has_out_grads and (op._id not in stop_ops):
          if is_func_call:
            func_call = ops.get_default_graph()._get_function(op.type)
            grad_fn = func_call.python_grad_func
            # pylint: enable=protected-access
          else:
            # A grad_fn must be defined, either as a function or as None
            # for ops that do not have gradients.
            try:
              grad_fn = ops.get_gradient_function(op)
            except LookupError:
              raise LookupError(
                  "No gradient defined for operation '%s' (op type: %s)" %
                  (op.name, op.type))
        if loop_state:
          loop_state.EnterGradWhileContext(op, before=False)
        if (grad_fn or is_func_call) and has_out_grads:
          # NOTE: If _AggregatedGrads didn't compute a value for the i'th
          # output, it means that the cost does not depend on output[i],
          # therefore dC/doutput[i] is 0.
          for i, out_grad in enumerate(out_grads):
            if (not isinstance(out_grad, ops.Tensor) and not out_grad) and (
                (not grad_fn and is_func_call) or _IsTrainable(op.outputs[i])):
              # Only trainable outputs or outputs for a function call that
              # will use SymbolicGradient get a zero gradient. Gradient
              # functions should ignore the gradient for other outputs.
              # TODO(apassos) gradients of resource handles might be an
              # issue here because of zeros.
              if loop_state:
                out_grads[i] = loop_state.ZerosLike(op, i)
              else:
                out_grads[i] = control_flow_ops.ZerosLikeOutsideLoop(op, i)
          with ops.name_scope(op.name + "_grad"):
            # pylint: disable=protected-access
            with ops.get_default_graph()._original_op(op):
              # pylint: enable=protected-access
              if grad_fn:
                # If grad_fn was found, do not use SymbolicGradient even for
                # functions.
                in_grads = _MaybeCompile(grad_scope, op, func_call,
                                         lambda: grad_fn(op, *out_grads))
              else: