)

python_repository(name = "python_version_repo")

load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION")

# Register multi toolchains
load("@rules_python//python:repositories.bzl", "python_register_toolchains")  # buildifier: disable=same-origin-load

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = TF_PYTHON_VERSION,
)

   While this isn't necessary for running the updater, it is required for
   actually using the new version with Tensorflow.

2) In the `WORKSPACE` file, add the new version to the `python_versions`
   parameter of the `python_register_multi_toolchains` function.

3) In the `BUILD.bazel` file, add a load statement for the new version, e.g.

   ```
      load("@python//3.11:defs.bzl",

        immediate_execution_ctx_.get(), 1.0f, &x2_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    x2.reset(x2_raw);
  }

  status_ = registry_.Register("IdentityN", IdentityNRegisterer);
  ASSERT_EQ(errors::OK, status_.code()) << status_.message();
  auto IdentityNGradModel = BuildGradModel(IdentityNModel, registry_);

  std::vector<AbstractTensorHandle*> outputs(2);

  // Returns whether `device_name` is registered as a custom device.
  virtual bool IsCustomDevice(const string& device_name) = 0;

  // Register a custom device. It will return error is the device name is
  // already registered.
  // TODO(tfrt-devs): Remove this method. Let caller register it directly into
  // CustomDeviceOpHandler.
  virtual Status RegisterCustomDevice(const string& name,

// call.
TF_AbstractFunction* TF_FinalizeFunction(TF_ExecutionContext* ctx,
                                         TF_OutputList*, TF_Status*);

void TF_DeleteAbstractFunction(TF_AbstractFunction*);

// Register the function with the given context. This is particularly useful for
// making a function available to an eager context.
void TF_ExecutionContextRegisterFunction(TF_ExecutionContext*,

  string name_;
};

static TracingContext* GraphTracingFactory(const char* name, TF_Status* s) {
  return new GraphContext(name);
}

// Register the tracing implemented in this file as the default tracing engine.
static bool register_tracing = [] {
  RegisterTracingEngineFactory("graphdef", GraphTracingFactory);
  SetDefaultTracingEngine("graphdef").IgnoreError();
  return true;
}();

# When cross-compiling with RBE, we need to copy the macOS sysroot to be
# inside the TensorFlow root directory. We then define them as a filegroup
# target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel
# can register it as an input to compile/link actions and send it to the remote
# VMs when needed.
# TODO(b/316932689): Avoid copying and replace with a local repository rule.
if [[ "$TFCI_MACOS_CROSS_COMPILE_ENABLE" == 1 ]]; then

#     visibility = ["//visibility:public"],
# )
# copybara:uncomment_end

# 'enable_registration_v2' opts-in to a different implementation of op and
# kernel registration - REGISTER_OP, REGISTER_KERNEL_BUILDER, etc.
#
# This setting is currently experimental. The 'v2' implementation does _not_
# correspond to a particular, finalized design; rather, it relates to
# developing one.
#

  TF_RETURN_IF_ERROR(
      op->Execute(absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));
  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {

TF_CAPI_EXPORT extern void
TF_ImportGraphDefOptionsSetValidateColocationConstraints(
    TF_ImportGraphDefOptions* opts, unsigned char enable);

// Load the library specified by library_filename and register the pluggable
// device and related kernels present in that library. This function is not
// supported on embedded on mobile and embedded platforms and will fail if
// called.
//