# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation
build --distinct_host_configuration=false

    const tensorflow::ServerDef& cluster_server_def, int task_index) {
  tensorflow::ServerDef single_host_server_def;
  single_host_server_def.set_job_name("worker");
  single_host_server_def.set_protocol(cluster_server_def.protocol());
  single_host_server_def.set_task_index(0);
  tensorflow::ClusterDef* cluster_def =
      single_host_server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->add_job();

    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_TensorHandle* on_host =
        TFE_TensorHandleCopyToDevice(copy_aliased, ctx, "CPU:0", status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TF_Tensor* resolved = TFE_TensorHandleResolve(on_host, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    const float* resolved_data =

# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation
build --distinct_host_configuration=false

  EXPECT_EQ(TF_GraphNumFunctions(host_graph_), 1);
  EXPECT_EQ(TF_GraphGetFunctions(host_graph_, funcs, 0, s_), 0);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  EXPECT_EQ(TF_GraphGetFunctions(host_graph_, funcs, 1, s_), 1);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  AssertEqual(func0, funcs[0]);
  TF_DeleteFunction(funcs[0]);

  // Returns number of elements across all dimensions.
  virtual Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.

// Returns the number of elements in dimension `dim_index`.
// Tensor representation on device can be transposed from its representation
// on host. The data contained in dimension `dim_index` on device
// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow
// major-to-minor order (slowest varying dimension first),

# 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

      environ_cp,
      var_name='GCC_HOST_COMPILER_PATH',
      var_default=default_gcc_host_compiler_path,
      ask_for_var='Please specify which gcc should be used by nvcc as the host '
      'compiler.',
      check_success=os.path.exists,
      resolve_symlinks=True,
      error_msg='Invalid gcc path. %s cannot be found.',
  )

  write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path)

  // Find and return a function record added by its name.
  virtual core::RefCountPtr<FunctionRecord> FindRecord(
      const string& name) const = 0;

  // Return the ParsedName of Host CPU device.
  virtual const DeviceNameUtils::ParsedName& HostCPUParsedName() const = 0;
  virtual const string& HostCPUName() const = 0;

  // Configure soft device placement policy.