/**
     * Default constructor for InputSource.
     */
    public InputSource() {}

    /**
     * Creates a new InputSource from an API model InputSource.
     * This constructor is used for converting between the API model and the compat model.
     *
     * @param source the API model InputSource to convert from
     */
    public InputSource(org.apache.maven.api.model.InputSource source) {

import com.google.common.annotations.GwtIncompatible;
import com.google.common.annotations.J2ktIncompatible;
import java.io.Serializable;
import org.jspecify.annotations.Nullable;

/**
 * Utility class for converting between various ASCII case formats. Behavior is undefined for
 * non-ASCII input.
 *
 * @author Mike Bostock
 * @since 1.0
 */
@GwtCompatible
public enum CaseFormat {
  /**

import com.google.common.annotations.GwtIncompatible;
import com.google.common.annotations.J2ktIncompatible;
import java.io.Serializable;
import org.jspecify.annotations.Nullable;

/**
 * Utility class for converting between various ASCII case formats. Behavior is undefined for
 * non-ASCII input.
 *
 * @author Mike Bostock
 * @since 1.0
 */
@GwtCompatible
public enum CaseFormat {
  /**

Because **FastAPI** doesn't make any changes to a `Response` you return, you have to make sure its contents are ready for it.

For example, you cannot put a Pydantic model in a `JSONResponse` without first converting it to a `dict` with all the data types (like `datetime`, `UUID`, etc) converted to JSON-compatible types.

For those cases, you can use the `jsonable_encoder` to convert your data before passing it to a response:

 * This class provides methods for converting binary data to readable hex format.
 */

public class Hexdump {

    /**
     * Default constructor.
     */
    public Hexdump() {
        // Utility class - no instance variables to initialize
    }

    /**
     * Array of hexadecimal digit characters used for converting binary data to hex representation.
     */

 */
package jcifs.util;

import jcifs.Encodable;

/**
 * Interface for objects that can be encoded to byte arrays.
 * Provides standardized method for converting objects to their binary representation.
 *
 * @author mbechler
 */
public class ByteEncodable implements Encodable {

    private final byte[] bytes;
    private final int off;
    private final int len;

#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

class FunctionRecord;

// A traced function: this hides the complexity of converting the serialized
// representation between various supported formats e.g. FunctionDef and Mlir
// function.
class AbstractFunction : public core::RefCounted {
 protected:
  enum AbstractFunctionKind { kGraph, kMlir };

  public void testBigToDouble() {
    for (BigInteger b : ALL_BIGINTEGER_CANDIDATES) {
      if (b.doubleValue() != DoubleUtils.bigToDouble(b)) {
        failFormat(
            "Converting %s to double: expected doubleValue %s but got bigToDouble %s",
            b, b.doubleValue(), DoubleUtils.bigToDouble(b));
      }
    }
  }

  public void testEnsureNonNegative() {

* `frozenset`:
    * In requests and responses, treated the same as a `set`:
        * In requests, a list will be read, eliminating duplicates and converting it to a `set`.
        * In responses, the `set` will be converted to a `list`.
        * The generated schema will specify that the `set` values are unique (using JSON Schema's `uniqueItems`).
* `bytes`:

- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.

### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.

### 5. (optional) Any other info / logs