@Override
    public boolean isRootDirectory(Path dir) {
        if (Files.isDirectory(dir.resolve(".mvn"))) {
            return true;
        }
        // we're too early to use the modelProcessor ...
        Path pom = dir.resolve("pom.xml");
        try (InputStream is = Files.newInputStream(pom)) {
            XMLStreamReader parser = XMLInputFactory.newFactory().createXMLStreamReader(is);

limitations under the License.
==============================================================================*/

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

#include <cstdint>

#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

/**
 * Interface used to locate the root directory for a given project.
 *
 * The root locator is usually looked up from the plexus container.
 * One notable exception is the computation of the early {@code session.rootDirectory}
 * property which happens very early.  The implementation used in this case
 * will be discovered using the JDK service mechanism.
 *
 * The default implementation will look for a {@code .mvn} child directory

#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_OPERATION_H_

#include <memory>

#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/cancellation.h"
#include "tensorflow/core/framework/device_attributes.pb.h"

#ifndef TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_
#define TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.

If you have a different use case that needs to exit early from a dependency with `yield`, please create a [GitHub Discussion Question](https://github.com/fastapi/fastapi/discussions/new?category=questions) with your specific use case and why you would benefit from having early closing for dependencies with `yield`.

#ifndef TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_

#include <memory>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {

// Abstract interface to a context.
//
// This serves as a factory for creating `AbstractOperation`s and for
// registering traced functions.

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

#include <string>

#include "include/dlpack/dlpack.h"  // from @dlpack
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/core/common_runtime/eager/tensor_handle.h"
#include "tensorflow/core/framework/tensor.h"

vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
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

#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/immediate_execution_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/eager/tfe_context_internal.h"
#include "tensorflow/c/eager/tfe_op_internal.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_buffer_internal.h"
#include "tensorflow/c/tf_status.h"