}


    @Override
    protected void engineUpdate ( byte b ) {
        this.md5.update(b);
    }


    @Override
    protected void engineUpdate ( byte[] input, int offset, int len ) {
        this.md5.update(input, offset, len);
    }

        {
            "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": [

  }
};

// An abstract operation describes an operation by its type, name, and
// attributes. It can be "executed" by the context with some input tensors.
// It is allowed to reusing the same abstract operation for multiple execution
// on a given context, with the same or different input tensors.
class TracingOperation : public AbstractOperation {
 protected:
  explicit TracingOperation(AbstractOperationKind kind)

      if (MappedRange.InlineDelta.MAX_VALUE >= abs(codePointDelta)) {
        return MappedRange.InlineDelta(mapping.rangeStart, codePointDelta)
      }
    }
  }
  return null
}

/**
 * Inputs must have applied [withoutSectionSpans].
 */
internal fun sections(mappings: List<Mapping>): Map<Int, List<MappedRange>> {
  val result = mutableMapOf<Int, MutableList<MappedRange>>()

  for (mapping in mappings) {

                                        <th style="width: 25%"><la:message
                                                key="labels.dict_stopwords_source"/></th>
                                        <td>${f:h(input)}<la:hidden property="input"/></td>
                                    </tr>
                                    </tbody>
                                </table>
                            </div>

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
 */

    }

    protected EditBody createEditBody(final SynonymItem entity, final String dictId) {
        final EditBody body = new EditBody();
        body.id = entity.getId();
        body.dictId = dictId;
        body.inputs = entity.getInputsValue();
        body.outputs = entity.getOutputsValue();
        return body;
    }

import java.util.Map.Entry;
import junit.framework.TestSuite;

/**
 * A generator of {@code TestSuite} instances for testing {@code CharSource} implementations.
 * Generates tests of all methods on a {@code CharSource} given various inputs the source is
 * expected to contain.
 *
 * @author Colin Decker
 */
@AndroidIncompatible // TODO(b/230620681): Make this available (even though we won't run it).

        Iterables.filter(
            FINITE_DOUBLE_CANDIDATES,
            new Predicate<Double>() {
              @Override
              public boolean apply(Double input) {
                return input.doubleValue() > 0.0;
              }
            });
    DOUBLE_CANDIDATES_EXCEPT_NAN = Iterables.concat(FINITE_DOUBLE_CANDIDATES, INFINITIES);

		b.Fatal(err)
	}

	// get text data generated for number of bytes equal to object size.
	textData := generateBytesData(objSize)
	// generate md5sum for the generated data.
	// md5sum of the data to written is required as input for PutObject.

	md5hex := getMD5Hash(textData)
	sha256hex := ""

	// benchmark utility which helps obtain number of allocations and bytes allocated per ops.
	b.ReportAllocs()