unsigned char enable,
                                                      TF_Status* status);

// Configure device placement policy logging for the eager executor. Note this
// policy is applied to any subsequent op executions.
TF_CAPI_EXPORT void TFE_ContextSetLogDevicePlacement(TFE_Context* ctx,

#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/tstring.h"
#include "tensorflow/core/protobuf/cluster.pb.h"

#include <vector>

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/tstring.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

// Return a tensor handle containing a float scalar

#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/lib/hash/hash.h"
#include "tensorflow/core/lib/strings/proto_serialization.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/str_util.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {

#include "absl/strings/str_cat.h"
#include "absl/types/variant.h"
#include "google/cloud/storage/client.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/experimental/filesystem/plugins/gcs/gcs_helper.h"
#include "tensorflow/c/logging.h"
#include "tensorflow/c/tf_status.h"

// Implementation of a filesystem for GCS environments.
// This filesystem will support `gs://` URI schemes.
namespace gcs = google::cloud::storage;

    *   Added APIs for switching between interactive logging and absl logging.
        By default, Keras always writes the logs to stdout. However, this is not
        optimal in a non-interactive environment, where you don't have access to
        stdout, but can only view the logs. You can use
        `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL
        logging. You can also use `tf.keras.utils.enable_interactive_logging()`

automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.

### Saved graphs and checkpoints

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you

/// operation `foo` might have a default implementation which uses `bar` and
/// `foobar`. If the plugin supplies `bar` and `foobar`, TensorFlow can use the
/// default implementation of `foo`.
///
/// During plugin loading, plugins will call the registration function provided
/// by this interface, supplying values for each of these structures. Core
/// TensorFlow checks that the plugin supplies all mandatory operations and

// supported on embedded on mobile and embedded platforms and will fail if
// called.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically
// loading a library. The rules for determining the exact location of the
// library are platform-specific and are not documented here.
//
// On success, returns the newly created library handle and places OK in status.