)
	ErrInvalidBatchKeyRotationWorkersWait = newErrFn(
		"Invalid value for batch key rotation workers wait",
		"Please input a non-negative duration",
		"keyrotation_workers_wait should be > 0ms",
	)
	ErrInvalidBatchReplicationWorkersWait = newErrFn(
		"Invalid value for batch replication workers wait",
		"Please input a non-negative duration",
		"replication_workers_wait should be > 0ms",
	)

                        saveBuffer();
                        const value = line.replace(promptLiteralStart, "").trimEnd();
                        useLines.push({
                            type: "input",
                            value: value
                        });
                    } else if (line.startsWith("// ")) {
                        saveBuffer();

        {
            "detail": [
                {
                    "type": "missing",
                    "loc": ["body", "files"],
                    "msg": "Field required",
                    "input": None,
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {

   * consistent hashing</a> for more information.
   */
  public static int consistentHash(long input, int buckets) {
    checkArgument(buckets > 0, "buckets must be positive: %s", buckets);
    LinearCongruentialGenerator generator = new LinearCongruentialGenerator(input);
    int candidate = 0;
    int next;

    // Jump from bucket to bucket until we go out of range
    while (true) {

  forward_op_->attrs.Reset(op);
  return op_->Reset(op, raw_device_name);
}
absl::Status AddInput(AbstractOperation* op_, AbstractTensorHandle* input,
                      ForwardOperation* forward_op_) {
  TF_RETURN_IF_ERROR(op_->AddInput(input));
  forward_op_->inputs.push_back(input);
  return absl::OkStatus();
}
absl::Status AddInputList(AbstractOperation* op_,

package com.google.common.math;

import java.math.BigInteger;
import java.util.Random;

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */

# Response Model - Return Type

You can declare the type used for the response by annotating the *path operation function* **return type**.

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

//// tab | Python 3.10+

```Python hl_lines="16  21"
{!> ../../docs_src/response_model/tutorial001_01_py310.py!}
```

  if (async) {
    if (forward_input) {
      // Since the input was forwarded, we released the input handle right away
      // and hence expect the input to be forwarded to the return tensor.
      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    } else {
      // In async mode we expect forwarding to work without releasing the input
      // handle since by the time the kernel is executed we have released the

        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["path", "item_id"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "foo",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [

        {
            "detail": [
                {
                    "type": "missing",
                    "loc": ["body", "file"],
                    "msg": "Field required",
                    "input": None,
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {