opts = TF_NewImportGraphDefOptions();
  TF_ImportGraphDefOptionsSetPrefix(opts, "imported");
  TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
  TF_ImportGraphDefOptionsAddInputMapping(opts, "fake", 0, {scalar, 0});

  tensorflow::GraphDef graph_def_proto;
  ASSERT_TRUE(tensorflow::ParseProtoUnlimited(&graph_def_proto, graph_def->data,
                                              graph_def->length));

    visibility = ["//visibility:public"],
)

# This condition takes precedence over :linux_x86_64
config_setting(
    name = "linux_x86_64_no_sse",
    values = {
        "cpu": "k8",
        "copt": "-mno-sse4.2",
    },
    visibility = ["//visibility:public"],
)

# This condition takes precedence over :linux_x86_64
# TODO(b/290533709): Remove this with PJRT build rule cleanup.

  ///
  /// The returned entries are paths relative to `path`.
  ///
  /// Plugins must allocate `entries` to hold all names that need to be returned
  /// and return the size of `entries`. Caller takes ownership of `entries`
  /// after the call.
  ///
  /// In case of error, plugins must set `status` to a value different than
  /// `TF_OK`, free memory allocated for `entries` and return -1.
  ///

    var_default: (String) default value string.
    ask_for_var: (String) string for how to ask for user input.
    check_success: (Function) function that takes one argument and returns a
      boolean. Should return True if the value provided is considered valid. May
      contain a complex error message if error_msg does not provide enough

build:windows --copt=/d2ReducedOptimizeHugeFunctions
build:windows --host_copt=/d2ReducedOptimizeHugeFunctions

# Enable the runfiles symlink tree on Windows. This makes it possible to build
# the pip package on Windows without an intermediate data-file archive, as the
# build_pip_package script in its current form (as of Aug 2023) uses the

//       .Attr("N: int >= 2")
//       .Attr("T: type");
// that defines two inputs, "concat_dim" and "values" (in that order).
// You must use TF_AddInput() for the first input (since it takes a
// single tensor), and TF_AddInputList() for the second input (since
// it takes a list, even if you were to pass a list with a single
// tensor), as in:
//   TF_OperationDescription* desc = TF_NewOperation(graph, "Concat", "c");