}

void TF_SetFullType(TF_Graph* graph, TF_Operation* op,
                    const TF_Buffer* full_type_proto) {
  using tensorflow::RecordMutation;
  mutex_lock l(graph->mu);
  FullTypeDef full_type;
  full_type.ParseFromArray(full_type_proto->data, full_type_proto->length);
  *op->node.mutable_def()->mutable_experimental_type() = full_type;
  RecordMutation(graph, *op, "setting fulltype");
}

    // Grad for Softmax Input
    TF_RETURN_IF_ERROR(BroadcastMul(
        ctx, grad_outputs[0], forward_outputs_[1],
        grad_inputs.subspan(0, 1)));  // upstream_grad * local softmax grad

    // Grad for labels is null
    grad_inputs[1] = nullptr;
    return absl::OkStatus();
  }
  ~SparseSoftmaxCrossEntropyWithLogitsGradientFunction() override {}

 private:
  vector<AbstractTensorHandle*> forward_outputs_;
};

  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(config, ret));
  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {
  tensorflow::RunOptions options;
  if (enable_full_trace) {
    options.set_trace_level(tensorflow::RunOptions::FULL_TRACE);
  } else {
    options.set_trace_level(tensorflow::RunOptions::NO_TRACE);
  }
  TF_Buffer* ret = TF_NewBuffer();

    for (int i = 0; i < entry.slices_size(); ++i) {
      const auto& slice_proto = entry.slices(i);
      CHECK(filtered_keys
                .insert(EncodeTensorNameSlice(
                    string(v2_reader_->key()) /* full var's name */,
                    TensorSlice(slice_proto)))
                .second);
    }
  }

  // Second pass: adds the entries, ignoring the filtered keys.

        std::vector<char> got;
        TF_EXPECT_OK(ReadCache(&cache, "", offset, n, &got));
        // Verify the size of the read.
        if (offset + n <= size) {
          // Expect a full read.
          EXPECT_EQ(got.size(), n) << "block size = " << block_size
                                   << ", offset = " << offset << ", n = " << n;
        } else {
          // Expect a partial read.

#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/test.h"

using tensorflow::Status;
using tensorflow::string;

  worker_server1.release();
  worker_server0.release();
}

// This test verifies the following:
// 1. Start the GRPC servers for both worker 0 and 1 using the full cluster
//    server def A.
// 2. Create a context B using the full cluster server def A.
// 3. Create the variable in B.
// 4. Create a context C using a single host server def D with only worker 1.
// 5. Read the variable in C.

  if (undeleted_files == nullptr || undeleted_dirs == nullptr)
    return errors::FailedPrecondition(
        "DeleteRecursively must not be called with `undeleted_files` or "
        "`undeleted_dirs` set to NULL");

  if (ops_->delete_recursively == nullptr)
    return FileSystem::DeleteRecursively(dirname, token, undeleted_files,
                                         undeleted_dirs);

  TF_SessionOptions* opt = TF_NewSessionOptions();
  TF_Status* s = TF_NewStatus();
  const char* tags[] = {tensorflow::kSavedModelTagServe};
  TF_Graph* graph = TF_NewGraph();
  // NULL run_options and meta_graph_def should work.
  TF_Session* session = TF_LoadSessionFromSavedModel(
      opt, nullptr, saved_model_dir.c_str(), tags, 1, graph, nullptr, s);
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  }

  dlm_tensor->dl_tensor.shape = shape_arr->data();
  // There are two ways to represent compact row-major data
  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();