std::unique_ptr<const TF_WritableFileOps> writable_file_ops,
      std::unique_ptr<const TF_ReadOnlyMemoryRegionOps>
          read_only_memory_region_ops,
      std::function<void*(size_t)> plugin_memory_allocate,
      std::function<void(void*)> plugin_memory_free)
      : filesystem_(std::move(filesystem)),
        ops_(std::move(filesystem_ops)),
        random_access_file_ops_(std::move(random_access_file_ops)),

    absl::Span<AbstractTensorHandle* const> inputs, int input_index,
    float* expected_grad, int num_grad, bool use_function,
    double abs_error = 1e-2) {
  Status s;
  AbstractTensorHandlePtr numerical_grad;
  {
    AbstractTensorHandle* numerical_grad_raw;
    s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function,
                          &numerical_grad_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

  return OkStatus();
}

// Validates the operations supplied by the plugin.
//
// Uses the 4 simpler `ValidateHelper(const TF_...*)` to validate each
// individual function table and then checks that the function table for a
// specific file type exists if the plugin offers support for creating that
// type of files.
static Status ValidateOperations(const TF_FilesystemPluginOps* ops) {

        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "graph_function",
    srcs = ["graph_function.cc"],
    hdrs = [
        "graph_function.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_function",
        "//tensorflow/core:framework",
        "//tensorflow/core/platform:refcount",

    TF_DeleteGraph(func_graph_);
    TF_DeleteStatus(s_);
  }

  const char* func_name_ = "MyFunc";
  TF_Status* s_;
  TF_Graph* func_graph_;
  TF_Function* func_;
};

TEST_F(CApiExperimentalFunctionTest, GraphRemoveFunction) {
  TF_Function* funcs[1];
  DefineFunction(func_name_, &func_);

  TF_GraphCopyFunction(func_graph_, func_, nullptr, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    x.reset(x_raw);
  }

  Status s = RunModel(TestScalarShape, ctx.get(),
                      /*inputs=*/{x.get()},
                      /*outputs=*/{},
                      /*use_function=*/UseFunction());
  ASSERT_EQ(errors::OK, s.code()) << s.message();
}

// Checks that inputs[0] is a matrix with shape 2x4.
Status TestTensorShape2x4(AbstractContext* ctx,

#
# Repacks libtensorflow tarballs into $DIR with provided $TARBALL_SUFFIX,
# and also repacks libtensorflow-src.jar into a standardized format.

# Helper function to copy a srcjar after moving any source files
# directly under the root to the "maven-style" src/main/java layout
#
# Source files generated by annotation processors appear directly

#define TENSORFLOW_C_EAGER_GRADIENTS_INTERNAL_H_

#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
namespace internal {

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.
// These APIs are mainly to facilitate testing and are subject to change.

            ] + tf_additional_binary_deps() +
            # TODO(b/259305727): Remove this select and include captured_function in macos builds.
            select({
                "//tensorflow:macos": [],
                "//conditions:default": [
                    "//tensorflow/core/data:captured_function",
                ],
            }),
    soversion = VERSION,
    static_deps = PACKAGE_STATIC_DEPS,

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.