// session has been run (this is detected in RecordMutation function). If the
  // string is empty, no mutation has occurred. Otherwise the string is a
  // description of the mutation suitable for returning to the user.
  //
  // Sessions are added to this map in TF_NewSession, and removed in
  // TF_DeleteSession.
  // TF_Graph may only / must be deleted when
  //   sessions.size() == 0 && delete_requested == true
  //

                absl::Span<AbstractTensorHandle*> outputs, bool use_function) {
  if (use_function) {
    const char* fn_name = "test_fn";
    core::RefCountPtr<AbstractFunction> scoped_func;
    // Returning null tensors from a tf.function is not supported, so we keep
    // track of indices in the model's outputs are nullptr in this set.
    // The FunctionDef only outputs the non-null tensors. We later pad the

// for each op.
// TODO(srbs): We need to define `ZerosLike` here to keep the compiler happy.
// Figure out a way to avoid this.
// TODO(srbs): Should ZerosLike check-fail instead of returning nullptr?
class TapeTensor {
 public:
  explicit TapeTensor(AbstractTensorHandle* handle);
  TapeTensor(const TapeTensor& other);
  ~TapeTensor();

  int64_t GetID() const;

                      &FloatDeallocator, nullptr);
}

// All the *Helper methods are used as a workaround for the restrictions that
// one cannot call ASSERT_* methods in non-void-returning functions (when
// exceptions are disabled during compilation)
void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name,
                       TF_DataType dtype, const std::vector<int64_t>& dims,

    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);
      if (!output) {
        return errors::Unimplemented(
            "Returning a non-graph tensor from a function has not "
            "been implemented yet.");
      }
      graph_outputs.push_back(output->output_);
    }

    auto s = TF_NewStatus();

  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.
Status RunAndMaybeSum(AbstractContext* ctx, Model forward,
                      absl::Span<AbstractTensorHandle* const> inputs,
                      absl::Span<AbstractTensorHandle*> outputs,

/// argument list with the out arguments. We only use the return type for an out
/// parameter if that is a plain C type, as this ensures ABI compatibility
/// (returning structures has issues in case compiler options affect
/// optimizations such as RVO). If a status needs to be returned from these
/// methods, the last argument is always a `TF_Status *` (or an array of such

-# if __GNUC_PREREQ (2, 91)
-#  define __bzero(s, n) __builtin_memset (s, '\0', n)
-# endif
-
+# define __bzero(s, n) __builtin_memset (s, '\0', n)
 #endif
 
-
-/* Copy N bytes from SRC to DEST, returning pointer to byte following the
-   last copied.  */
-#ifdef __USE_GNU
-# if !defined _HAVE_STRING_ARCH_mempcpy || defined _FORCE_INLINES
-#  ifndef _HAVE_STRING_ARCH_mempcpy
-#   if __GNUC_PREREQ (3, 4)

    TF_Operation* op;
    UseHelper(inputs, &op);
    return op;
  }

  // All the *Helper methods are used as a workaround for the restrictions that
  // one cannot call ASSERT_* methods in non-void-returning functions (when
  // exceptions are disabled during compilation)
  void UseHelper(const std::vector<TF_Output>& inputs, TF_Operation** op) {
    TF_OperationDescription* desc =

        `tf.keras.utils.image_dataset_from_directory`,`tf.keras.utils.text_dataset_from_directory`,
        and `audio_dataset_from_directory`, to be used with the
        `validation_split` argument, for returning both dataset splits at once,
        as a tuple.
    *   Added `tf.keras.utils.split_dataset` utility to split a `Dataset` object
        or a list/tuple of arrays into two `Dataset` objects (e.g. train/test).