T data[] = {value};
  return TestTensorHandleWithDims<T, datatype>(ctx, data, nullptr, 0);
}

// Return a tensor handle containing a 100x100 matrix of floats
TFE_TensorHandle* TestMatrixTensorHandle100x100(TFE_Context* ctx);

// Return a tensor handle containing a 3x2 matrix of doubles
TFE_TensorHandle* DoubleTestMatrixTensorHandle3X2(TFE_Context* ctx);

// Return a tensor handle containing a 3x2 matrix of floats

  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* n = TestMatrixTensorHandle100x100(ctx);
  // If a GPU exists, copy the handle to GPU so that we can exercise
  // unprotecting a mirror.
  std::string gpu_device_name;
  if (GetDeviceName(ctx, &gpu_device_name, "GPU")) {

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteTensor(t);
  TF_DeleteStatus(status);
  return th;
}

TFE_TensorHandle* TestMatrixTensorHandle100x100(TFE_Context* ctx) {
  constexpr int64_t dims[] = {100, 100};
  constexpr int num_elements = dims[0] * dims[1];
  float data[num_elements];
  for (int i = 0; i < num_elements; ++i) {
    data[i] = 1.0f;

Yujing Zhang <******@****.***> 1597190920 -0700

Yujing Zhang <******@****.***> 1607726961 -0800

  // Use large matrices so that RPCs don't return before we get a chance
  // to call TFE_DeleteContext.
  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle100x100(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle100x100(ctx);
  const char remote_device_name[] =
      "/job:localhost/replica:0/task:1/device:CPU:0";
  auto* h0_task1 =

Gaurav Jain <******@****.***> 1586210724 -0700

A. Unique TensorFlower <******@****.***> 1728709129 -0700

Allen Lavoie <******@****.***> 1598571038 -0700

A. Unique TensorFlower <******@****.***> 1759988790 -0700