cc_library(
    name = "modular_filesystem",
    srcs = [
        "modular_filesystem.cc",
        "modular_filesystem_registration.cc",
    ],
    hdrs = [
        "modular_filesystem.h",
        "modular_filesystem_registration.h",
    ],
    # TODO(b/139060984): Visibility should be more restrictive once we
    # convert to modular filesystems everywhere
    visibility = ["//visibility:public"],

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"):
  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v2/3.
_tf_uses_legacy_keras = (

==============================================================================*/

#include "absl/log/log.h"
#include "tensorflow/c/experimental/filesystem/filesystem_interface.h"
#include "tensorflow/c/experimental/filesystem/modular_filesystem_registration.h"
#include "tensorflow/c/experimental/filesystem/plugins/posix/posix_filesystem.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

# lazy loading.
_current_module.compat.v2  # pylint: disable=pointless-statement

# 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"):
  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v1.
_tf_uses_legacy_keras = (

  }

  // Populate return_nodes
  DCHECK(tf_results->return_nodes.empty());
  tf_results->return_nodes.resize(results.return_nodes.size());
  for (int i = 0; i < results.return_nodes.size(); ++i) {
    tf_results->return_nodes[i] = ToOperation(results.return_nodes[i]);
  }

  // Populate missing unused map keys
  DCHECK(tf_results->missing_unused_key_names.empty());

        ],
        "//conditions:default": [
            ":env",
            ":logging",
            ":tf_status",
            ":tf_tensor",
            "//tensorflow/c/experimental/filesystem:modular_filesystem",
            "//tensorflow/cc:grad_ops",
            "//tensorflow/cc:gradients",
            "//tensorflow/cc:ops",
            "//tensorflow/cc:scope_internal",
            "//tensorflow/cc:while_loop",

        selective op registration headers else execution would fail with
        unregistered kernels error.

*   Modular File System Migration:

    *   Support for S3 and HDFS file systems has been migrated to a modular file
        systems based approach and is now available in
        https://github.com/tensorflow/io. The `tensorflow-io` python package

        "//tensorflow/c/experimental/filesystem:modular_filesystem",
        "//tensorflow/core/platform:status",
        "@com_google_absl//absl/log",
    ],
    alwayslink = 1,
)

# Library implementing helper functionality, so that the above only contains
# the API implementation for modular filesystems.
cc_library(
    name = "posix_filesystem_helper",

# For projects which use TensorFlow as part of a Bazel build process, putting
# nothing in a bazelrc will default to a monolithic build. The following line
# opts in to modular op registration support by default.
build --define framework_shared_object=true
build --define tsl_protobuf_header_only=true

build --define=use_fast_cpp_protos=true
build --define=allow_oversize_protos=true

    float grad_data[1];
    memcpy(&grad_data[0], TF_TensorData(grad_tensor),
           TF_TensorByteSize(grad_tensor));
    TF_DeleteTensor(grad_tensor);
    dtheta_approx[i] = grad_data[0];
  }

  // Populate *numerical_grad with the data from dtheta_approx.
  TF_RETURN_IF_ERROR(TestTensorHandleWithDims<float, TF_FLOAT>(
      ctx, dtheta_approx.data(), theta_dims.data(), num_dims, numerical_grad));
  TF_DeleteTensor(theta_tensor);