Please fill out either the individual or corporate Contributor License Agreement (CLA).

  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).

        "memory regions");

  return OkStatus();
}

// Validates the operations supplied by the plugin.
//
// Uses the 4 simpler `ValidateHelper(const TF_...*)` to validate each
// individual function table and then checks that the function table for a
// specific file type exists if the plugin offers support for creating that
// type of files.
static Status ValidateOperations(const TF_FilesystemPluginOps* ops) {

                                                    TF_Status* status);
// Get an attribute type given an op name; a fusion of TFE_NewOp and
// TFE_OpGetAttrType for use from Python without the overhead of the individual
// calls and memory management of TFE_Op.
TF_CAPI_EXPORT extern TF_AttrType TFE_OpNameGetAttrType(
    TFE_Context* ctx, const char* op_or_function_name, const char* attr_name,

    print('Found no ResultStore links for Bazel build/test invocations.')
  else:
    print(f'Found {len(result_store_dict)} ResultStore link(s) for '
          f'Bazel invocations.\n'
          f'ResultStore contains individual representations of each target '
          f'that were run/built during the invocation.\n'
          f'These results are generally easier to read than looking through '
          f'the entire build log:\n')
  i = 1

  // Helper to copy a tensor handle from another device once for each component
  // of the ParallelDevice.
  //
  // Sets a bad status and returns a nullptr if `tensor` is already on the
  // ParallelDevice, or if the individual copies fail.
  std::unique_ptr<ParallelTensor> CopyToParallelDevice(TFE_Context* context,
                                                       TFE_TensorHandle* tensor,

    *   `tf.data.experimental.service.DispatchServer` now takes a config tuple
        instead of individual arguments. Usages should be updated to
        `tf.data.experimental.service.DispatchServer(dispatcher_config)`.
    *   `tf.data.experimental.service.WorkerServer` now takes a config tuple
        instead of individual arguments. Usages should be updated to
        `tf.data.experimental.service.WorkerServer(worker_config)`.

  // Define the functions
  TF_Function* func;
  TF_Function* grad_func;
  DefineFunction("FooFunc", &func);
  DefineFunction("MyGrad", &grad_func);

  // Add functions individually
  TF_GraphCopyFunction(host_graph_, func, nullptr, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  TF_GraphCopyFunction(host_graph_, grad_func, nullptr, s_);