<div class="form-inline col-sm-9">
                                        <la:errors property="depth"/>
                                        <input type="number" name="depth" id="depth"
                                               value="${f:h(depth)}" class="form-control"
                                               min="0" max="10000">

	)
	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",
	)

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!}
```

                        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": [
                {

        {
            "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": [
                {

    private static final long serialVersionUID = 0;
  }

  /**
   * This is adapted from the regex suggested by {@link Double#valueOf(String)} for prevalidating
   * inputs. All valid inputs must pass this regex, but it's semantically fine if not all inputs
   * that pass this regex are valid -- only a performance hit is incurred, not a semantics bug.
   */
  @GwtIncompatible // regular expressions
  static final

    /**
     * Calculates the effective repositories for the given input repositories which are assumed to be already mirrored
     * (if applicable). This process will essentially remove duplicate repositories by merging them into one equivalent
     * repository. It is worth to point out that merging does not simply choose one of the input repositories and
     * discards the others but actually combines their possibly different policies.