Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate

		}
	}
	return seq
}

// Test tests parses endpoint ellipses input pattern.
func TestParseEndpointSet(t *testing.T) {
	testCases := []struct {
		arg     string
		es      endpointSet
		success bool
	}{
		// Tests invalid inputs.
		{
			"...",
			endpointSet{},
			false,
		},
		// No range specified.
		{
			"{...}",

                                    <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",
	)

                        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": [
                {

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!}
```

   * input element is mapped to one cell in the returned table, with the rows, columns, and values
   * generated by applying the specified functions.
   *
   * <p>The returned {@code Collector} will throw a {@code NullPointerException} at collection time
   * if the row, column, or value functions return null on any input.
   *
   * @since 33.2.0 (available since 21.0 in guava-jre)
   */

        {
            "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": [