limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradient_checker.h"

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_tensor.h"

namespace tensorflow {
namespace gradients {

package lifecycle

import (
	"bytes"
	"encoding/xml"
)

// Status represents lifecycle configuration status
type Status string

// Supported status types
const (
	Enabled  Status = "Enabled"
	Disabled Status = "Disabled"
)

// Rule - a rule for lifecycle configuration.
type Rule struct {
	XMLName             xml.Name            `xml:"Rule"`
	ID                  string              `xml:"ID,omitempty"`

  }

  /**
   * Returns an adapter that expects this value wrapped by another value. Typically this occurs
   * when a value has both a context or application tag and a universal tag.
   *
   * Use this for EXPLICIT tag types:
   *
   * ```
   * [5] EXPLICIT UTF8String
   * ```
   *
   * @param forceConstructed non-null to set the constructed bit to the specified value, even if the

    this.type = type;
    this.annotations = ImmutableList.copyOf(annotations);
    this.annotatedType = annotatedType;
  }

  /** Returns the type of the parameter. */
  public TypeToken<?> getType() {
    return type;
  }

  /** Returns the {@link Invokable} that declares this parameter. */
  public Invokable<?, ?> getDeclaringInvokable() {
    return declaration;
  }

  @Override

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

 * reference. It allows you to represent "a {@code T} that must be present" and a "a {@code T} that
 * might be absent" as two distinct types in your program, which can aid clarity.
 *
 * <p>Some uses of this class include
 *
 * <ul>
 *   <li>As a method return type, as an alternative to returning {@code null} to indicate that no
 *       value was available

        for (JavaPathType type : JavaPathType.values()) {
            if (location.equals(type.location)) {
                return Optional.of(type);
            }
        }
        return Optional.empty();
    }

    /**
     * Returns the name of the tool option for this path. For example, if this path type
     * is {@link #MODULES}, then this method returns {@code "--module-path"}. The option

#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/tstring.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

// Return a tensor handle containing a float scalar
TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, float value);

   * the cache, but only the "generic" one is type-safe. That is, it will properly prevent you from
   * building caches whose key or value types are incompatible with the types accepted by the
   * weigher already provided; the {@code CacheBuilder} type cannot do this. For best results,
   * simply use the standard method-chaining idiom, as illustrated in the documentation at top,

fix. Even if you think it's relatively minor, it's helpful to know what people
are working on. And as mentioned above, API changes should be discussed
thoroughly before moving to code.

Some examples of types of pull requests that are immediately helpful:

  - Fixing a bug without changing a public API.
  - Fixing or improving documentation.
  - Improvements to Maven configuration.

Guidelines for any code contributions: