// have all gradients available. This set usually is a subset of the set of
// targets (not all since targets which have outputs in the tape will not have
// gradients available initially).
//
// Then we repeatedly pop an entry from the stack, run its backprop, and update
// the gradients of its inputs. Once we have computed all gradients for a single

    int64_t init_timeout_in_ms, TF_Status* status,
    bool clear_existing_contexts);

// Set server def with retries and timeout. This is helpful for fault-tolerant
// initial connection in high-preemption environments, such as
// ParameterServerStrategy training.
// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeoutAndRetries(

      converts them into `NoOp`s instead.

*   `tf.data`

    * `tf.data` now has an `autotune_options.initial_parallelism` option to
      control the initial parallelism setting used by autotune before the data
      pipeline has started running. The default is 16. A lower value reduces
      initial memory usage, while a higher value improves startup time.

## Keras

*  `keras.layers.experimental.DynamicEmbedding`

-		      || (__builtin_constant_p (s2)			      \
-			  && strlen (s2) < ((size_t) (n))))		      \
-		  ? strcmp (s1, s2) : strncmp (s1, s2, n)))
 #endif
 
 
 /* Return the length of the initial segment of S which
    consists entirely of characters not in REJECT.  */
-#if !defined _HAVE_STRING_ARCH_strcspn || defined _FORCE_INLINES
-# ifndef _HAVE_STRING_ARCH_strcspn
-#  if __GNUC_PREREQ (3, 2)

// Return a tensor handle containing a 3x2 matrix of floats
TFE_TensorHandle* TestMatrixTensorHandle3X2(TFE_Context* ctx);

// Return a variable handle referring to a variable with the given initial value
// on the given device.
TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name = "");

// Return an add op multiplying `a` by `b`.

  if (TF_GetCode(status) != TF_OK) {
    return nullptr;
  }

  // Dump the op creation stacktraces for debugging purpose.
  DEBUG_DATA_DUMPER()->DumpOpCreationStackTraces(
      fn_name, kDebugGroupOpStacktrace, "initial", &fn_body->graph);

  tensorflow::StackTracesMap stack_traces;
  for (const Node* n : fn_body->graph.nodes()) {
    stack_traces[n->name()] = n->GetStackTrace();
  }

  // for created operations.
  const char* name;
} TF_WhileParams;

// Creates a TF_WhileParams for creating a while loop in `g`. `inputs` are
// outputs that already exist in `g` used as initial values for the loop
// variables.
//
// The returned TF_WhileParams will have all fields initialized except
// `cond_output`, `body_outputs`, and `name`. The `body_outputs` buffer will be