{
                    "type": "string_pattern_mismatch",
                    "loc": ["query", "item-query"],
                    "msg": "String should match pattern '^fixedquery$'",
                    "input": "nonregexquery",
                    "ctx": {"pattern": "^fixedquery$"},
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

                {
                    "type": "string_pattern_mismatch",
                    "loc": ["query", "item-query"],
                    "msg": "String should match pattern '^fixedquery$'",
                    "input": "nonregexquery",
                    "ctx": {"pattern": "^fixedquery$"},
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

                                    <div class="col-sm-9">
                                        <la:errors property="attributes.gidNumber"/>
                                        <input type="number" id="attributes.gidNumber" name="attributes.gidNumber" class="form-control"/>
                                    </div>
                                </div>
                            </div>

// parallel device creates a parallel tensor `x` with `a` on the first of
// `underlying_devices` and `b` on the second. Running `a_unpacked, b_unpacked =
// TPUReplicatedOutput(input=x, num_replicas=2)` un-packs the parallel tensor
// into its components.
//
// The filled `device` struct and the allocated `device_info` struct may be
// passed to TFE_RegisterCustomDevice. The `device_name` arguments must match.

    String getId();

    String getName();

    String[] getPermissions();

    String[] getVirtualHosts();

    String getDocumentBoost();

    String getIndexingTarget(String input);

    String getConfigId();

    Integer getTimeToLive();

    CrawlerClientFactory initializeClientFactory(Supplier<CrawlerClientFactory> creator);

    Map<String, String> getConfigParameterMap(ConfigName name);

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["cookie", "santa_tracker"],
            "msg": "Extra inputs are not permitted",
            "input": "good-list-please",
        }
    ]
}
```

## Resumo

dValidator({name:"confirmation",validatorFunction:function(b,c,d,e,f){var g,h=c.valAttr("confirm")||c.attr("name")+"_confirmation",i=f.find('[name="'+h+'"]');if(!i.length)return a.formUtils.warn('Password confirmation validator: could not find an input with name "'+h+'"',!0),!1;if(g=i.val(),d.validateOnBlur&&!i[0].hasValidationCallback){i[0].hasValidationCallback=!0;var j=function(){c.validate()};i.on("keyup",j),f.one("formValidationSetup",function(){i[0].hasValidationCallback=!1,i.off("keyup",j)})}return...

                "--unsafe-fixes",
                "-",
            ],
            input=base_content.encode("utf-8"),
            capture_output=True,
        )
        content_target = version_result.stdout.decode("utf-8")
        format_result = subprocess.run(
            [find_ruff_bin(), "format", "-"],
            input=content_target.encode("utf-8"),
            capture_output=True,
        )

    requests with the actual input and output keys used by the trainer code, or
    conversely, update the trainer code to name the input and output Tensors
    'inputs' and 'outputs', respectively. Signatures using the 'classify' and
    'regress' methods are not affected by this change; they will continue to
    standardize their input and output keys as before.
*   Add in-memory caching to the Dataset API.

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["header", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus",
        }
    ]
}
```

## Summary