"tf_tensor.h",
        "tf_tstring.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_internal",
        ":c_api_macros_hdrs",
        ":c_api_no_xla",
        ":tf_attrtype",
        ":tf_buffer",
        ":tf_file_statistics",
        ":tf_status_helper",
        ":tf_status_internal",
        ":tf_tensor_internal",
        ":tf_tstring",

    }
    TF_Status* s = TF_NewStatus();
    auto* operation = TF_FinishOperation(tf_opdesc, s);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(s));
    TF_DeleteStatus(s);
    *num_retvals = TF_OperationNumOutputs(operation);
    for (int i = 0; i < *num_retvals; ++i) {
      retvals[i] = new GraphTensor({operation, i}, g_);
    }
    return absl::OkStatus();
  }

  if (ctx == nullptr) {
    return;
  }

  // ctx->RefCountIsOne() should be true here.
  tensorflow::unwrap(ctx)->Release();
}

TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) {
  TF_DeviceList* l = new TF_DeviceList;
  tensorflow::unwrap(ctx)->ListDevices(&l->response);
  return l;
}

void TFE_ContextClearCaches(TFE_Context* ctx) {

    return;
  }
  auto* state_iter = static_cast<TF_Status*>(states);
  for (size_t i = 0; i < tasks.length; ++i) {
    const auto& result = (*results)[i];
    TF_Status s;
    TF_SetStatus(&s, static_cast<TF_Code>(result.error_code()),
                 std::string(result.error_message()).data());
    if (TF_GetCode(&s) != TF_Code::TF_OK) {
      tensorflow::CoordinationServiceError error;

load(
    "//tensorflow:tensorflow.bzl",
    "tf_cc_test",
    "tf_copts",
    "tf_cuda_cc_test",
    "tf_cuda_library",
)
load("//tensorflow:tensorflow.default.bzl", "cc_header_only_library", "filegroup")
load(
    "//tensorflow/core/platform:build_config_root.bzl",
    "tf_cuda_tests_tags",
)
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(

  return r->length;
}

}  // namespace tf_read_only_memory_region

// SECTION 4. Implementation for `TF_Filesystem`, the actual filesystem
// ----------------------------------------------------------------------------
namespace tf_posix_filesystem {

static void Init(TF_Filesystem* filesystem, TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");
}

static void Cleanup(TF_Filesystem* filesystem) {}

  // `TF_AttrBuilderCheckCanRunOnDevice()` call(s) on the same `builder`.
  std::set<std::string> attr_names;
};

TF_AttrBuilder* TF_NewAttrBuilder(const char* op_name) {
  return new TF_AttrBuilder(op_name);
}

void TF_DeleteAttrBuilder(TF_AttrBuilder* builder) { delete builder; }

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {

    can test it by passing that file's path to
    [tf_doctest.py](https://www.tensorflow.org/code/tensorflow/tools/docs/tf_doctest.py).
    For example:

    ```bash
    python tf_doctest.py --file=<file_path>
    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)

    stack_traces[n->name()] = n->GetStackTrace();
  }

  TF_Function* tf_function = new TF_Function();
  tf_function->record = new tensorflow::FunctionRecord(
      std::move(fdef), std::move(stack_traces), false);

  return tf_function;
}

TF_Function* TF_GraphToFunction(const TF_Graph* fn_body, const char* fn_name,
                                unsigned char append_hash_to_fn_name,

    const std::vector<Gradient*>& in_grads, absl::Span<Gradient*> out_grads) {
  /* This function is approximately equivalent to this Python code:

  forwardprop_aids = tf.ones_like(output_tensors)
  with tf.GradientTape() as g:
    g.watch(forwardprop_aids)
    grad = backward_function(forwardprop_aids)
  forward_grads = g.gradient(grad, forwardprop_aids, output_gradients=in_grads)