// Import it in a fresh graph.
  TF_DeleteGraph(graph);
  graph = TF_NewGraph();
  TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions();
  TF_GraphImportGraphDef(graph, graph_def, opts, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Operation* scalar = TF_GraphOperationByName(graph, "scalar");

  // Import it in a fresh graph with an unused input mapping.

    } else {
      // In sync mode, forwarding can't really happen since the client code will
      // have a reference count on the input tensor while the kernel is being
      // executed and thus it cannot be re-used for the return tensor.
      EXPECT_NE(orig_ptr, TF_TensorData(t));
    }
  }

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteTensorHandle(m);

// don't want to have a `std::vector<std::string>` at global level, we use a
// static pointer to such a vector: we construct it via `SchemeVector()` below
// and when tests are instantiated we process it using `GetSchemes()`.
static std::vector<std::string>* SchemeVector() {
  static std::vector<std::string>* schemes = new std::vector<std::string>;
  return schemes;
}

///
/// All memory allocated by the plugin that will be owned by core TensorFlow
/// must be allocated using the allocator in this structure. Core TensorFlow
/// will use the deallocator to free this memory once it no longer needs it.
///
/// IMPORTANT: To maintain binary compatibility, the layout of this structure
/// must not change! In the unlikely case that new global operations must be
/// provided, add them at the end of the structure.

}

// Specification for an expected edge.
// src is either:
// - input name (as it appears in FunctionDef)
// - name of output tensor (in nested "add:z:0" format)
// dst is either:
// - output name (as it appears in FunctionDef)
// - <name_of_node>:<index_of_this_input_into_node> (this looks the same as
//      output tensor naming, but it the index is actually an input index)
struct EdgeSpec : public std::pair<string, string> {

# with clang. It does not imply that CUDA support has been enabled.
alias(
    name = "is_cuda_compiler_clang",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_compiler_clang",
        "@local_config_cuda//cuda:TRUE",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled
# with nvcc. It does not imply that CUDA support has been enabled.
alias(

# START CROSS-COMPILE CONFIGS
# Set execution platform to Linux x86
# Note: Lot of the "host_" flags such as "host_cpu" and "host_crosstool_top"
# flags seem to be actually used to specify the execution platform details. It
# seems it is this way because these flags are old and predate the distinction
# between host and execution platform.
build:cross_compile_base --host_cpu=k8

// `run_metadata`) are valid.
//
//    - `run_options` may be NULL, in which case it will be ignored; or
//      non-NULL, in which case it must point to a `TF_Buffer` containing the
//      serialized representation of a `RunOptions` protocol buffer.
//    - `run_metadata` may be NULL, in which case it will be ignored; or
//      non-NULL, in which case it must point to an empty, freshly allocated

      break
    elif not os.path.exists(python_bin_path):
      print('Invalid python path: {} cannot be found.'.format(python_bin_path))
    else:
      print('{} is not executable.  Is it the python binary?'.format(
          python_bin_path))
    environ_cp['PYTHON_BIN_PATH'] = ''

  # Convert python path to Windows style before checking lib and version
  if is_windows() or is_cygwin():