_current_module = _sys.modules[__name__]
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.
# Import compat before trying to import summary from tensorboard, so that
# reexport_tf_summary can get compat from sys.modules. Only needed if using
# lazy loading.

_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
_current_module = _sys.modules[__name__]

if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.

# Load tensorflow-io-gcs-filesystem if enabled
if (_os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "true" or
    _os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "1"):

  ops->writable_file_ops = static_cast<TF_WritableFileOps*>(
      plugin_memory_allocate(TF_WRITABLE_FILE_OPS_SIZE));
  ops->writable_file_ops->cleanup = tf_writable_file::Cleanup;
  ops->writable_file_ops->append = tf_writable_file::Append;
  ops->writable_file_ops->tell = tf_writable_file::Tell;
  ops->writable_file_ops->flush = tf_writable_file::Flush;
  ops->writable_file_ops->sync = tf_writable_file::Sync;

  if is_linux():
    if int(environ_cp.get('TF_NEED_TENSORRT', False)):
      cuda_libraries.append('tensorrt')
    if environ_cp.get('TF_NCCL_VERSION', None):
      cuda_libraries.append('nccl')
  if is_windows():
    if int(environ_cp.get('TF_NEED_TENSORRT', False)):
      cuda_libraries.append('tensorrt')
      print('WARNING: TensorRT support on Windows is experimental\n')

    control_output_nodes.push_back(&control_outputs[i]->node);
  }

  // Do the actual function creation.
  DCHECK(append_hash_to_fn_name <= 1);
  tensorflow::FunctionDef fdef;
  status->status = tensorflow::GraphToFunctionDef(
      fn_body->graph, fn_name, append_hash_to_fn_name != 0,
      /*set_stateful_from_nodes=*/true,
      /*copy_placeholder_attrs_from_nodes=*/true, body_nodes, input_tensors,

// DT_RESOURCE. The string serialization is converted back to a
// ResourceHandle during Session run where the TF_Tensor is converted to a
// Tensor.
// TFv2 does not depend on this conversion. There is no matching
// TF_TensorFromTensorV1 because the conversion to string is performed by the
// python side of Session.
static Status TF_TensorToTensorV1(const TF_Tensor* src, Tensor* dst) {

        ],
        "//conditions:default": [],
    }) + if_tensorrt([
        "//tensorflow/compiler/tf2tensorrt:trt_convert_api",
    ]),
)

# Check that c_api_no_xla does not depend on xla.
check_deps(
    name = "c_api_no_xla_check_deps",
    disallowed_deps = ["//tensorflow/compiler/jit:xla_kernel_creator"],
    deps = [":c_api_no_xla"],
)

tf_cuda_library(

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

# To avoid duplication, check that the C++ or python library does not depend on
# the stream executor cuda plugins. Targets that want to use cuda APIs should
# instead depend on the dummy plugins in @local_tsl//tsl/platform/default/build_config
# and use header only targets.
# TODO(ddunleavy): This seems completely broken. :tensorflow_cc depends on

        *   Added a new output mode `"one_hot"` for `CategoryEncoding`,
            `StringLookup`, `IntegerLookup`, which will encode each element in
            an input batch individually, and automatically append a new output
            dimension if necessary. Use this mode on rank 1 inputs for the old
            `"binary"` behavior of one-hot encoding a batch of scalars.

      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.
      bazel cquery "rdeps(kind(py_test, $(cat $BATS_TEST_TMPDIR/deps)), $dep, 1)"
    done < $BATS_TEST_TMPDIR/missing_deps
    exit 1
  fi
}

# The Python package is not allowed to depend on any CUDA packages.