}

void TF_DeleteAbstractOp(TF_AbstractOp* op) { unwrap(op)->Release(); }

void TF_DeleteAbstractTensor(TF_AbstractTensor* t) { unwrap(t)->Unref(); }

TF_OutputList* TF_NewOutputList() { return wrap(new OutputList); }
void TF_DeleteOutputList(TF_OutputList* o) { delete unwrap(o); }
void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {

  TF_WritableFileHandle* h;
  TF_Status* status = TF_NewStatus();
  TF_NewWritableFile(full_path, &h, status);
  if (TF_GetCode(status) != TF_OK) {
    fprintf(stderr, "TF_NewWritableFile failed: %s\n", TF_Message(status));
    return 1;
  }
  fprintf(stderr, "wrote %s\n", full_path);
  free(full_path);
  TF_CloseWritableFile(h, status);
  if (TF_GetCode(status) != TF_OK) {

    python3 -m pip install "$TF_WHEEL"
}

@test "TensorFlow is importable" {
    source /tf/venv/bin/activate
    python3 -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
}

# Is this still useful?
@test "TensorFlow has Keras" {
    source /tf/venv/bin/activate
    python3 -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'

                         status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  TensorHandlePtr value_one(FloatTensorHandle(3., status.get()));
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TensorHandlePtr value_two(FloatTensorHandle(-2., status.get()));
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

namespace tf_random_access_file {

// TODO(b/139060984): Implement later

}  // namespace tf_random_access_file

// SECTION 2. Implementation for `TF_WritableFile`
// ----------------------------------------------------------------------------
namespace tf_writable_file {

// TODO(b/139060984): Implement later

}  // namespace tf_writable_file

// SECTION 3. Implementation for `TF_ReadOnlyMemoryRegion`

    auto num_elem_analytical = TF_TensorElementCount(analytical_tensor);

    ASSERT_EQ(num_elem_numerical, num_elem_analytical);

    float* dnumerical = new float[num_elem_numerical]{0};
    memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),
           TF_TensorByteSize(numerical_tensor));
    float* danalytical = new float[num_elem_analytical]{0};
    memcpy(&danalytical[0], TF_TensorData(analytical_tensor),

                                          /*result=*/outputs));
  exp_output->Unref();
  return absl::OkStatus();
}

TEST_P(CustomGradientTest, ExpWithPassThroughGrad) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  AbstractContextPtr ctx;
  {
    AbstractContext* ctx_raw = nullptr;
    Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);

            CACHEBUSTER=${{ steps.tf-version.outputs.DATE }}
          tags: |
            tensorflow/build:latest-${{ matrix.python-version }}
            tensorflow/build:${{ steps.tf-version.outputs.TF_VERSION }}-${{ matrix.python-version }}
            gcr.io/tensorflow-sigs/build:latest-${{ matrix.python-version }}
            gcr.io/tensorflow-sigs/build:${{ steps.tf-version.outputs.TF_VERSION }}-${{ matrix.python-version }}

TF_CAPI_EXPORT extern TF_VariableInfo* TF_CreateVariableInfoFromContext(
    TF_OpKernelContext* ctx, int index, TF_Status* status);

TF_CAPI_EXPORT extern void TF_LockVariableInfos(TF_VariableInfo** vars,
                                                int num_vars,
                                                TF_Status* status);

TF_CAPI_EXPORT extern void TF_AllocateTempForVariableInfo(

# Experimental gcs filesystem plugin.
load("//tensorflow:tensorflow.bzl", "get_win_copts", "tf_cc_shared_object", "tf_cc_test")
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

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

# Filesystem implementation for GCS environments
tf_cc_shared_object(
    name = "gcs_filesystem",
    framework_so = [],