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(

// 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`.

  // 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

#include <stddef.h>
#include <stdint.h>

#include "tensorflow/c/tf_file_statistics.h"
#include "tensorflow/c/tf_status.h"

/// This is the interop header between core TensorFlow and modular filesystem
/// plugins (see initial RFC https://github.com/tensorflow/community/pull/101).
///
/// Both core TensorFlow and every plugin will use this header. The associated
/// `.cc` file is only used by core TensorFlow to implement checking needed for

// 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