-		     ? (char *) memcpy (dest, src, n)			      \
-		     : strncpy (dest, src, n))				      \
-		  : strncpy (dest, src, n)))
-# endif
-#endif
-
-
 /* Append no more than N characters from SRC onto DEST.  */
 #ifndef _HAVE_STRING_ARCH_strncat
 # ifdef _USE_STRING_ARCH_strchr
@@ -780,380 +438,29 @@ __stpcpy_small (char *__dest,
 		       : (*((char *) __mempcpy (strchr (__dest, '\0'),	      \

                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per

    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

2.  If you are changing the code and the docstring of a class/function/method,
    then you will need to

  EXPECT_TRUE(executed);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_DeleteTensorHandle(retval);

  // Explicit placement still forces the op onto the requested device
  matmul.reset(MatMulOp(context.get(), hcustom0.get(), hcpu.get()));
  TFE_OpSetDevice(matmul.get(), "/job:localhost/replica:0/task:0/device:CPU:0",
                  status.get());

  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may
  // be an empty string if no device was explicitly requested, but will
  // otherwise be the name of this custom device. Ops are placed onto a custom
  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,

 public:
  using GradientTape<AbstractTensorHandle, GradientFunction,
                     TapeTensor>::GradientTape;
  // Returns whether the tape is persistent, i.e., whether the tape will hold
  // onto its internal state after a call to `ComputeGradient`.
  using GradientTape<AbstractTensorHandle, GradientFunction,
                     TapeTensor>::IsPersistent;

  // Adds this tensor to the list of watched tensors.

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//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/job:localhost/replica:0/task:0/device:GPU:1"}
// Then executing an operation on CUSTOM:0 will execute it on GPU:0 and GPU:1.
//
// Implicit copies onto `device_name` are allowed, replicating the value once
// per device in `underlying_devices`. Implicit copies off of the device throw
// an error.
//
// All component tensors must have the same dtype. Currently they must also have

#include "tensorflow/core/platform/test.h"

namespace {

struct LoggingDevice {
  tensorflow::string device_name;
  tensorflow::string underlying_device;
  // Set to true whenever a TensorHandle is copied onto the device
  bool* arrived_flag;
  // Set to true whenever an operation is executed
  bool* executed_flag;
  // If true, only explicit op placements are accepted. If false, uses
  // type-based dispatch.

  TensorHandlePtr value_one(FloatTensorHandle(1., status.get()));
  TensorHandlePtr value_two(FloatTensorHandle(2., status.get()));
  {
    // Try to pack two TensorHandles onto a parallel device with a single
    // component.
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    std::array<TFE_TensorHandle*, 2> components{value_one.get(),