common:cuda_clang --config=cuda
common:cuda_clang --@local_config_cuda//:cuda_compiler=clang
common:cuda_clang --copt=-Qunused-arguments
# Select supported compute capabilities (supported graphics cards).
# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX

				"application/vnd.oasis.opendocument.database",
				"application/vnd.oasis.opendocument.formula",
				"application/vnd.oasis.opendocument.formula-template",
				"application/vnd.oasis.opendocument.graphics",
				"application/vnd.oasis.opendocument.graphics-template",
				"application/vnd.oasis.opendocument.image",
				"application/vnd.oasis.opendocument.image-template",
				"application/vnd.oasis.opendocument.presentation",

// --------------------------------------------------------------------------
// The new graph construction API, still under development.

// Represents a computation graph.  Graphs may be shared between sessions.
// Graphs are thread-safe when used as directed below.
typedef struct TF_Graph TF_Graph;

// Return a new graph object.
TF_CAPI_EXPORT extern TF_Graph* TF_NewGraph(void);

 * cache instances created by {@code CacheBuilder} will not perform any type of eviction.
 *
 * <p>Usage example:
 *
 * {@snippet :
 * LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
 *     .maximumSize(10000)
 *     .expireAfterWrite(Duration.ofMinutes(10))
 *     .removalListener(MY_LISTENER)
 *     .build(
 *         new CacheLoader<Key, Graph>() {

  // Add func and its gradient to host graph
  TF_GraphCopyFunction(host_graph_, func_, grad_func, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Verify that function and its grad are in host graph's GraphDef
  GraphDef gdef;
  GetGraphDef(host_graph_, &gdef);
  std::vector<std::string> func_names = GetFuncNames(gdef);
  ASSERT_EQ(2, func_names.size());
  ASSERT_EQ(func_name_, func_names[0]);

 * cache instances created by {@code CacheBuilder} will not perform any type of eviction.
 *
 * <p>Usage example:
 *
 * {@snippet :
 * LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
 *     .maximumSize(10000)
 *     .expireAfterWrite(Duration.ofMinutes(10))
 *     .removalListener(MY_LISTENER)
 *     .build(
 *         new CacheLoader<Key, Graph>() {

    // Compare that the graphs match.
    GraphDef expected_gdef;
    GraphDef gdef;
    EXPECT_TRUE(GetGraphDef(expected_graph_, &expected_gdef));
    EXPECT_TRUE(GetGraphDef(graph_, &gdef));
    TF_EXPECT_GRAPH_EQ(expected_gdef, gdef);

    // Compare that the output of the gradients of both graphs match.
    RunGraphsAndCompareOutputs(grad_outputs, expected_grad_outputs);

/*!
  * Bootstrap v5.3.0 (https://getbootstrap.com/)
  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
  */

/*!
  * Bootstrap v4.6.2 (https://getbootstrap.com/)
  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
  */

  // Add a config_proto attr, to trigger grappler graph rewrites in the current
  // eager runtime.
  if (enable_grappler) {
    tensorflow::ConfigProto config;
    // Do not skip grappler optimization even for small graphs.
    config.mutable_graph_options()
        ->mutable_rewrite_options()
        ->set_min_graph_nodes(-1);
    string serialized_config;
    ASSERT_TRUE(config.SerializeToString(&serialized_config));