if (undeleted_files == nullptr || undeleted_dirs == nullptr)
    return errors::FailedPrecondition(
        "DeleteRecursively must not be called with `undeleted_files` or "
        "`undeleted_dirs` set to NULL");

  if (ops_->delete_recursively == nullptr)
    return FileSystem::DeleteRecursively(dirname, token, undeleted_files,
                                         undeleted_dirs);

// is for performance optimization by reusing an exiting unused op rather than
// creating a new op every time. If `raw_device_name` is `NULL` or empty, it
// does not set the device name. If it's not `NULL`, then it attempts to parse
// and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster
// than separately calling it because if the existing op has the same

    gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
    absl::Span<AbstractTensorHandle*> result) const {
  if (gradient_function == nullptr) {
    return errors::InvalidArgument(
        "Provided null gradient_function for '", op_type, "'.\n",
        "If the intent is to treat this op as non-differentiable consider "
        "using RegisterNotDifferentiable or "
        "NotDifferentiableGradientFunction.");
  }

TF_CAPI_EXPORT extern void TFE_OpSetAttrType(TFE_Op* op, const char* attr_name,
                                             TF_DataType value);
// If the number of dimensions is unknown, `num_dims` must be set to
// -1 and `dims` can be null.  If a dimension is unknown, the
// corresponding entry in the `dims` array must be -1.
TF_CAPI_EXPORT extern void TFE_OpSetAttrShape(TFE_Op* op, const char* attr_name,

  const string malformed_text_proto(R"(cluster {
  job {
    name: "worker")");
  TF_Buffer* null_result =
      TFE_GetServerDef(malformed_text_proto.c_str(), status);
  EXPECT_NE(TF_GetCode(status), TF_OK);
  EXPECT_TRUE(absl::StrContains(TF_Message(status),
                                "Invalid text proto for ServerDef"));
  EXPECT_EQ(null_result, nullptr);

  // Cleanup
  TF_DeleteBuffer(result);
  TF_DeleteStatus(status);
}

    status->status =
        tensorflow::errors::InvalidArgument("Unparseable AttrValue proto");
    return;
  }
  if (op == nullptr) {
    status->status = tensorflow::errors::InvalidArgument(
        "Got a null or uninitialized `op` argument");
    return;
  }
  tensorflow::EagerOperation* operation =
      OperationFromInterface(tensorflow::unwrap(const_cast<TFE_Op*>(op)));

  // between calls to ShouldRecord and Accumulator), and its outputs
  // (`output_tensors`).
  //
  // If provided, a non-null `forward_function` will be used instead of the
  // backward function (`backward_function_getter` /
  // `backward_function_deleter`) to compute jvps for this operation. If
  // `forward_function` is null, a GradientTape is used on the backward function
  // to compute the jvp, which will waste computation when executing eagerly.

-      ? ((__builtin_constant_p (s) && __string2_1bptr_p (s))		      \
-	 ? __builtin_strpbrk (s, accept)				      \
-	 : ((__a0 = ((const char  *) (accept))[0], __a0 == '\0')	      \
-	    ? ((void) (s), (char *) NULL)				      \
-	    : ((__a1 = ((const char *) (accept))[1], __a1 == '\0')	      \
-	       ? __builtin_strchr (s, __a0)				      \
-	       : ((__a2 = ((const char *) (accept))[2], __a2 == '\0')	      \

# TODO(b/279852433) Replace deps(//tensorflow/...) with deps(//...)
@test "Verify that it's possible to query every TensorFlow target without BUILD errors" {
    bazel query "deps(//tensorflow/...)" > /dev/null
}

teardown_file() {
    bazel shutdown

  return ret;
}

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//
// If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto,
// before creating a TF_Function out of the possibly mutated proto.
static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto(
    const char* text_proto,