if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

  // case placement information is lost when resolving the handle.
  //
  // If false, a caller might implement pretty-printing by resolving and
  // iterating over the resulting tensor. This may still be viable if resolving
  // the handle loses information, but `SummarizeValue` would be more precise.
  virtual bool PreferCustomSummarizer() const { return false; }

              "localhost:", tensorflow::testing::PickUnusedPortOrDie())});

  tensorflow::JobDef* job_def2 = cluster_def->add_job();
  job_def2->set_name("worker");

  // Copy over `host:port` at `task_index`
  for (auto task : cluster_server_def.cluster().job(0).tasks()) {
    if (task.first == task_index) {
      job_def2->mutable_tasks()->insert({task.first, task.second});
    }
  }

}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});
  TestEncodeDecode(__LINE__, {"hello"});
  TestEncodeDecode(__LINE__,
                   {"the", "quick", "brown", "fox", "jumped", "over"});

  string big(1000, 'a');
  TestEncodeDecode(__LINE__, {"small", big, "small2"});
}

TEST(CAPI, SessionOptions) {
  TF_SessionOptions* opt = TF_NewSessionOptions();
  TF_DeleteSessionOptions(opt);

However, for the vast majority of issues, we aim to empower individuals to first resolve conflicts themselves, asking for help when needed, and only after that fails to escalate further. This approach gives people more control over the outcome of their dispute. 

If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:

//   However since convenience is not a primary goal, we don't do this.
// * Devices are not in this API.  Instead, they are created/used internally
//   and the API just provides high level controls over the number of
//   devices of each type.

#ifdef __cplusplus
extern "C" {
#endif

// --------------------------------------------------------------------------

// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.
// TODO: Migrate to TF_CreateConfig() below.
TF_CAPI_EXPORT extern void TF_EnableXLACompilation(TF_SessionOptions* options,

          {"SparseSoftmaxCrossEntropyWithLogits", {1}},
          {"FusedBatchNorm", {1, 2, 3, 4}},
      });
  return m;
}

}  // namespace

// If over kMinAggregateCount gradients are accumulated and the total
// memory consumption is over kMinAggregateBytes, do an early aggregation
// so as to release the gradient tensor to save memory.
constexpr int kMinAggregateCount = 4;

                                 suppress_default_error=False,
                                 resolve_symlinks=False,
                                 n_ask_attempts=_DEFAULT_PROMPT_ASK_ATTEMPTS):
  """Loop over user prompts for an ENV param until receiving a valid response.

  For the env param var_name, read from the environment or verify user input
  until receiving valid input. When done, set var_name in the environ_cp to its

}

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//
// If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto,
// before creating a TF_Function out of the possibly mutated proto.
static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto(
    const char* text_proto,