int* num_retvals, TF_Status* status);

// Add a function (serialized FunctionDef protocol buffer) to ctx so
// that it can be invoked using TFE_Execute.
TF_CAPI_EXPORT extern void TFE_ContextAddFunctionDef(
    TFE_Context* ctx, const char* serialized_function_def, size_t size,
    TF_Status* status);

FROM mcr.microsoft.com/dotnet/framework/sdk:4.8-windowsservercore-ltsc2019

# Set default powershell policy for this script (ProgressPreference='SilentlyContinue' makes
# downloads with Invoke-WebRequest not show the progress bar and is MUCH faster).
SHELL ["powershell.exe", "-ExecutionPolicy", "Bypass", "-Command", "$ErrorActionPreference='Stop'; $ProgressPreference='SilentlyContinue'; $VerbosePreference = 'Continue';"]

    TF_CoordinationServiceAgent* agent, TF_Status* status) {
  if (timeout_seconds <= 0) {
    status->status = absl::InvalidArgumentError(
        "TF_CoordinationServiceGetKeyValueWithTimeout invoked with invalid "
        "timeout_seconds <= 0.");
    return nullptr;
  }
  auto* cc_agent = reinterpret_cast<tsl::CoordinationServiceAgent*>(agent);
  auto value = cc_agent->GetKeyValue(std::string_view(key, key_size),

// ServerDef protocol buffer provided via `proto` and `proto_len`.
//
// The server will not serve any requests until TF_ServerStart is invoked.
// The server will stop serving requests once TF_ServerStop or
// TF_DeleteServer is invoked.
TF_CAPI_EXPORT extern TF_Server* TF_NewServer(const void* proto,
                                              size_t proto_len,

```

#### Option B: Paste your code here or provide a link to a custom end-to-end colab

```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

### 3. Failure after conversion

                                                      "callback1"));

  TFE_CancelCallback callback2;
  bool callback2_invoked = false;
  callback2.context = &callback2_invoked;
  callback2.callback = [](void* context) {
    *reinterpret_cast<bool*>(context) = true;
  };
  TFE_CancellationToken token2 = TFE_CancellationManagerGetToken(c_mgr);

called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

# Here is a single-line example of running a script on Linux to build the

                                      std::unique_ptr<CustomDevice> device) = 0;

  // Return FunctionLibraryDefinition. Transformations need to use it to use it
  // to invoke MLIR compiler passes.
  virtual FunctionLibraryDefinition* FuncLibDef() = 0;

  // Resets the global rendezvous used for functions.
  virtual void ResetGlobalRendezvousForFunction() = 0;

build:rbe_cross_compile_base_xla --config=rbe_cross_compile_base
build:rbe_cross_compile_base_xla --strategy=Genrule=standalone

# Due to the above strategy, all Genrule commands are executed locally, but the
# following actions invoke tools (E.g `flatc`, `llvm-tblgen`, etc.) that are
# only executabe on the RBE (x86) machine, so the strategy_regexp options are
# added to override and run the actions using remote strategy.

    void (*delete_func)(void*), TF_Status* status);

// If the ResourceMgr provided by the `ctx` has a resource
// `plugin_resource_name`, returns it in `*result_plugin_resource`. Otherwise,
// invokes create_func to create the resource. `delete_func` is needed for
// ResourceMgr to clean up the resource. `status` will be set. If `status` is
// not OK, `*result_plugin_resource` will be set as nullptr.
//