source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
  # replace tensorflow to tf_nightly in the wheel name

#include "tensorflow/c/experimental/filesystem/plugins/gcs/gcs_filesystem.h"

#include <stdlib.h>
#include <string.h>

#include <variant>

#include "absl/strings/numbers.h"
#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"

  }

 private:
  /// The size of the blocks stored in the LRU cache, as well as the size of the
  /// reads from the underlying filesystem.
  const size_t block_size_;
  /// The maximum number of bytes (sum of block sizes) allowed in the LRU cache.
  const size_t max_bytes_;
  /// The maximum staleness of any block in the LRU cache, in seconds.
  const uint64_t max_staleness_;

    char* buf;
    int buf_length;
  } buffer_val;
} TF_Filesystem_Option_Value_Union;

typedef struct TF_Filesystem_Option_Value {
  int type_tag;    // type of values in the values union
  int num_values;  // number of values
  TF_Filesystem_Option_Value_Union*
      values;  // owned (plugins must make a copy if storing this)
} TF_Filesystem_Option_Value;

typedef enum TF_Filesystem_Option_Type {

    TF_Status* status);

// ----------------------------  PJRT  -----------------------------------------
// Passes the pointer to a vector of PJRT_NamedValue and number of options to
// set options for creating a PJRT client. Passes nullptr for create_options and
// 0 for num_options if no options need to be set. You can use
// ConvertToPjRtNamedValueList in

    *   Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.sample_from_datasets()` operation, which...

#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/framework/numeric_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/platform.h"
#include "tensorflow/core/platform/status.h"

    return tensorflow::ops::Identity(ctx, input, output, name);
  } else if (!DataTypeIsComplex(BaseType(dtype)) &&
             BaseType(dtype) != DT_VARIANT) {
    return errors::InvalidArgument(
        "Expected numeric or variant tensor, got dtype ", dtype);
  }
  return tensorflow::ops::Conj(ctx, input, output, name);
}

class AddGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,

  }

  void VerifyFDefNodes(const tensorflow::FunctionDef& fdef,
                       const std::unordered_set<string>& nodes) {
    ASSERT_EQ(nodes.size(), fdef.node_def_size())
        << "Got unexpected number of nodes. Expected: ["
        << absl::StrJoin(nodes, ", ")
        << "] Actual nodes in fdef: " << fdef.DebugString();
    for (const NodeDef& node_def : fdef.node_def()) {

filegroup(
    name = "tensorflow_def_file",
    srcs = [":tf_custom_op_library_additional_deps.dll"],
    output_group = "def_file",
)

# Filter the DEF file to reduce the number of symbols to 64K or less.
# Note that we also write the name of the pyd file into DEF file so that
# the dynamic libraries of custom ops can find it at runtime.
genrule(
    name = "tensorflow_filtered_def_file",