message: [XLA:Python] Guard NCCL usage by if_cuda rather than if_nccl.
  if_nccl turns out to be true for non-GPU builds too.
  Technically there are cases where CUDA is enabled but NCCL is not, but these cases do not apply when building XLA:Python.
diff: load("//tensorflow/core/platform:build_config.bzl", "pyx_library")
  load("//tensorflow/compiler/xla:xla.bzl", "xla_py_test_deps")
  load("//tensorflow:tensorflow.bzl", "py_test", "tf_cc_test")
  -
  -# buildifier: disable=same-origin-load
  -load("//tensorflow:tensorflow.bzl", "if_nccl")
  +load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
  # buildifier: disable=same-origin-load
  load("//tensorflow:tensorflow.bzl", "pybind_extension")
  @@ -298,7 +296,7 @@ cc_library(
  name = "nvidia_gpu_device",
  srcs = ["nvidia_gpu_device.cc"],
  hdrs = ["nvidia_gpu_device.h"],
  - copts = if_nccl(["-DNCCL_ENABLED=1"]),
  + copts = if_cuda(["-DNCCL_ENABLED=1"]),
  deps = [
  ":local_client",
  "//tensorflow/compiler/xla/service/gpu:gpu_executable_run_options",
  @@ -310,7 +308,7 @@ cc_library(
  "//tensorflow/core:bfc_allocator",
  "//tensorflow/core:gpu_mem_allocator",
  "//tensorflow/stream_executor:tf_allocator_adapter",
  - ] + if_nccl(["@local_config_nccl//:nccl"]),
  + ] + if_cuda(["@local_config_nccl//:nccl"]),
  )
  config_setting(
  