@Override
        public boolean incrementToken() throws IOException {
            return tokenizer.incrementToken();
        }

        @Override
        public void end() throws IOException {
            tokenizer.end();
        }

        @Override
        public int hashCode() {
            return tokenizer.hashCode();
        }

        @Override

package jstream

import (
	"fmt"
	"strconv"
)

// Predefined errors
var (
	ErrSyntax        = DecoderError{msg: "invalid character"}
	ErrUnexpectedEOF = DecoderError{msg: "unexpected end of JSON input"}
	ErrMaxDepth      = DecoderError{msg: "maximum recursion depth exceeded"}
)

type errPos [2]int // line number, byte offset where error occurred

// DecoderError contains a detailed decoding error.
type DecoderError struct {

-negative:0!important;flex-shrink:0!important}.flex-shrink-1{-ms-flex-negative:1!important;flex-shrink:1!important}.justify-content-start{-ms-flex-pack:start!important;justify-content:flex-start!important}.justify-content-end{-ms-flex-pack:end!important;justify-content:flex-end!important}.justify-content-center{-ms-flex-pack:center!important;justify-content:center!important}.justify-content-between{-ms-flex-pack:justify!important;justify-content:space-between!important}.justify-content-around{-m...

// Return the number of outputs in the list.
int TF_OutputListNumOutputs(TF_OutputList* o);
// Return the `i`th output in the list.
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i);
// Append a tensor at the end of the output list, growing its size by one.
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,
                           TF_Status*);

    name: str
    description: Optional[str] = None
    model_b: ModelB

    @validator("name")
    def lower_username(cls, name: str, values):
        if not name.endswith("A"):
            raise ValueError("name must end in A")
        return name


async def get_model_c() -> ModelC:
    return ModelC(username="test-user", password="test-password")


@app.get("/model/{name}", response_model=ModelA)

	}

	var count6 int64
	if err := DB.Model(&User{}).Where("name in ?", []string{user1.Name, user2.Name, user3.Name}).Select(
		"(CASE WHEN name=? THEN ? ELSE ? END) as name", "count-1", "main", "other",
	).Count(&count6).Find(&users).Error; err != nil || count6 != 3 {
		t.Fatalf(fmt.Sprintf("Count should work, but got err %v", err))
	}

        append = true;
      }
      dummy ^= sb.toString().length();
    }
    return dummy;
  }

  /**
   * Starts with an empty delimiter and changes to the desired value at the end of the iteration.
   */
  @Benchmark
  int assignDelimiter(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      StringBuilder sb = new StringBuilder();
      String delim = "";

// The lines yielded by the iterator include their terminating newlines.
// If s is empty, the iterator yields no lines at all.
// If s does not end in a newline, the final yielded line will not end in a newline.
// It returns a single-use iterator.
func Lines(s []byte) iter.Seq[[]byte] {
	return func(yield func([]byte) bool) {
		for len(s) > 0 {
			var line []byte

repos:
-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v5.0.0
    hooks:
    -   id: check-added-large-files
    -   id: check-toml
    -   id: check-yaml
        args:
        -   --unsafe
    -   id: end-of-file-fixer
    -   id: trailing-whitespace
-   repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.7.1
    hooks:
    -   id: ruff
        args:
        - --fix
    -   id: ruff-format
ci:

 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * hold the numerical gradient data at the end of the function.
 */
absl::Status CalcNumericalGrad(AbstractContext* ctx, Model forward,
                               absl::Span<AbstractTensorHandle* const> inputs,