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

        {
            "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": [

  If var_name has been set as environment variable, use the preset value, else
  ask for user input. If no input is provided, the default is used.

  Args:
    environ_cp: copy of the os.environ.
    var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
    ask_for_var: string for how to ask for user input.
    var_default: default value string.

  Returns:
    string value for var_name
  """

    /* Set up defaults and ensure empty lines are displayed. */
    content: '';
    display: inline-block;
    vertical-align: middle;
}

[data-ty="input"]:before,
[data-ty-prompt]:before {
    margin-right: 0.75em;
    color: var(--color-text-subtle);
}

[data-ty="input"]:before {
    content: '$';
}

[data-ty][data-ty-prompt]:before {
    content: attr(data-ty-prompt);
}

[data-ty-cursor]:after {

                                        <div class="form-check">
                                            <la:checkbox styleId="jobLogging" styleClass="form-check-input" property="jobLogging"/>
                                            <label for="jobLogging" class="form-check-label">
                                                <la:message key="labels.enabled"/>

    device_name_ = name;
    return absl::OkStatus();
  }

  absl::Status AddInput(AbstractTensorHandle* input) override {
    GraphTensor* t = dyn_cast<GraphTensor>(input);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    TF_AddInput(op_.get(), t->output_);
    return absl::OkStatus();
  }
  absl::Status AddInputList(

    public static OptionalEntity<StopwordsItem> createStopwordsItem(final CreateForm form, final VaErrorHook hook) {
        return getEntity(form).map(entity -> {
            final String newInput = form.input;
            validateStopwordsString(newInput, "input", hook);
            entity.setNewInput(newInput);
            return entity;
        });
    }

        "Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

  VLOG(1) << "Running the enqueue op";
  TF_Output input{placeholder_op, 0};
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ &input, /*input_values*/ &tensor, /*ninputs*/ 1,
                // output related parameters

    assert response.json() == {
        "detail": [
            {
                "type": "string_type",
                "loc": ["tags", 3],
                "msg": "Input should be a valid string",
                "input": {"sneaky": "object"},
            }
        ]
    }


@needs_pydanticv2
def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")

			idx, err = strconv.Atoi(pool)
			if err != nil {
				// We didn't find any matching pools, invalid input
				writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, errInvalidArgument), r.URL)
				return
			}
		} else {
			idx = globalEndpoints.GetPoolIdx(pool)
			if idx == -1 {
				// We didn't find any matching pools, invalid input
				writeErrorResponseJSON(ctx, w, toAdminAPIErr(ctx, errInvalidArgument), r.URL)
				return
			}
		}