#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/file_system_helper.h"
#include "tsl/platform/errors.h"

// TODO(b/139060984): After all filesystems are converted, all calls to
// methods from `FileSystem` will have to be replaced to calls to private
// methods here, as part of making this class a singleton and the only way to
// register/use filesystems.

namespace tensorflow {

  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after
  // `keep_alive_secs` of inactivity.
  virtual Status SetOrUpdateServerDef(const ServerDef& server_def,
                                      bool reset_context, int keep_alive_secs,

  TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(ctx));
  if (!tracing_ctx) {
    tsl::Set_TF_Status_from_Status(
        s, tensorflow::errors::InvalidArgument(
               "Only TracingContext can be converted into a function."));
    return nullptr;
  }
  tsl::Set_TF_Status_from_Status(s,
                                 tracing_ctx->Finalize(unwrap(outputs), &func));
  TF_DeleteExecutionContext(ctx);

// TF_TensorToTensorV1 decodes a string serialization to DT_RESOURCE.
// In the TFv1 convention, TF_Tensor can hold a string serialization of
// 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

        // operations which supposedly both created the same Tensor. It comes up
        // in recompute_grad, where the gradients have the same value. However,
        // only the original gradient is connected to everything else, so we
        // should still use that.
        vspace_.DeleteGradient(forward_grads[i]);
      } else {
        accumulated_gradients_[output_tensors[i].GetID()] = forward_grads[i];
      }

// Create a TF_Function from a TF_Graph
//
// Params:
//  fn_body - the graph whose operations (or subset of whose operations) will be
//            converted to TF_Function.
//  fn_name - the name of the new TF_Function. Should match the operation
//            name (OpDef.name) regexp [A-Z][A-Za-z0-9_.\\-/]*.

  Version strings of the form X.YZ and X.Y.Z-xxxxx are supported. The
  'xxxxx' part, for instance 'homebrew' on OS/X, is ignored.

  Args:
    version: a version to be converted

  Returns:
    An integer if converted successfully, otherwise return None.
  """
  version = version.split('-')[0]
  version_segments = version.split('.')
  # Treat "0.24" as "0.24.0"
  if len(version_segments) == 2:

# Experimental filesystem C APIs for TensorFlow.
# Will be moved in proper place once all filesystems are converted to the
# modular framework.
load("//tensorflow:tensorflow.bzl", "tf_cc_test")
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

# This is only for plugins
cc_library(
    name = "filesystem_interface",

  virtual ContextDevicePlacementPolicy GetDevicePlacementPolicy() const = 0;

  // Configure graph collection in RunMetadata.
  virtual void SetShouldStoreGraphs(bool value) = 0;

  // Return the collected RunMetadata. This method will transfer the ownership
  // to the caller.
  virtual std::unique_ptr<RunMetadata> ExportRunMetadata() = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {

        inputs inside of the first positional argument.
    *   Several classes of TF ops that were not reliably converted to Keras
        layers during functional API construction should now work,
        e.g.`tf.image.ssim_multiscale`
    *   Error messages when Functional API construction goes wrong (and when ops
        cannot be converted to Keras layers automatically) should be clearer and
        easier to understand.