updater.write("# " + line);
                    }
                    continue;
                }

                final String input = m.group(1).trim();
                final String output = m.group(2).trim();

                if (input == null || output == null) {
                    logger.warn("Failed to parse {} to {}", line, path);
                    if (updater != null) {

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
                     const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

  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

# 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!}
```

        {
            "detail": [
                {
                    "type": "missing",
                    "loc": ["body", "file"],
                    "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": [

			metaMap[formCanonicalName] = policy.Value
		}
	}
	// Check if any extra metadata field is passed as input
	for key := range formValues {
		if strings.HasPrefix(key, "X-Amz-Meta-") {
			if _, ok := metaMap[key]; !ok {
				return fmt.Errorf("Invalid according to Policy: Extra input fields: %s", key)
			}
		}
	}

	// Flag to indicate if all policies conditions are satisfied
	var condPassed bool

			d := divisibleSize / cnt
			if d <= prevD {
				prevD = d
				setSize = cnt
			}
		}
	}
	return setSize
}

// possibleSetCountsWithSymmetry returns symmetrical setCounts based on the
// input argument patterns, the symmetry calculation is to ensure that
// we also use uniform number of drives common across all ellipses patterns.

        }

        for (final Pattern pattern : includedDocUrlPatterns) {
            if (pattern.matcher(input).matches()) {
                return Constants.TRUE;
            }
        }

        for (final Pattern pattern : excludedDocUrlPatterns) {
            if (pattern.matcher(input).matches()) {
                return Constants.FALSE;
            }
        }

        return Constants.TRUE;
    }

                "detail": [
                    {
                        "type": "missing",
                        "loc": ["cookie", "session_id"],
                        "msg": "Field required",
                        "input": {},
                    }
                ]
            }
        )
        | IsDict(
            # TODO: remove when deprecating Pydantic v1
            {
                "detail": [
                    {