class_docstr: Efficiently implements a subset of MLflow Tracking's  `MlflowClient` and fluent APIs to provide
  automatic batching and async execution of run operations by way of queueing, as well as
  parameter / tag truncation for autologging use cases. Run operations defined by this client,
  such as `create_run` and `log_metrics`, enqueue data for future persistence to MLflow
  Tracking. Data is not persisted until the queue is flushed via the `flush()` method, which
  supports synchronous and asynchronous execution.
  
  MlflowAutologgingQueueingClient is not threadsafe; none of its APIs should be called
  concurrently.
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docstr: Flushes all queued run operations, resulting in the creation or mutation of runs
  and run data.
  
  Args:
      synchronous: If `True`, run operations are performed synchronously, and a
          `RunOperations` result object is only returned once all operations
          are complete. If `False`, run operations are performed asynchronously,
          and an `RunOperations` object is returned that represents the ongoing
          run operations.
  
  Returns:
      A `RunOperations` instance representing the flushed operations. These operations
      are already complete if `synchronous` is `True`. If `synchronous` is `False`, these
      operations may still be inflight. Operation completion can be synchronously waited
      on via `RunOperations.await_completion()`.
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code: def flush(self, synchronous=True):
      """
      Flushes all queued run operations, resulting in the creation or mutation of runs
      and run data.
  
      Args:
          synchronous: If `True`, run operations are performed synchronously, and a
              `RunOperations` result object is only returned once all operations
              are complete. If `False`, run operations are performed asynchronously,
              and an `RunOperations` object is returned that represents the ongoing
              run operations.
  
      Returns:
          A `RunOperations` instance representing the flushed operations. These operations
          are already complete if `synchronous` is `True`. If `synchronous` is `False`, these
          operations may still be inflight. Operation completion can be synchronously waited
          on via `RunOperations.await_completion()`.
      """
      logging_futures = []
      for pending_operations in self._pending_ops_by_run_id.values():
          future = _AUTOLOGGING_QUEUEING_CLIENT_THREAD_POOL.submit(
              self._flush_pending_operations,
              pending_operations=pending_operations,
          )
          logging_futures.append(future)
      self._pending_ops_by_run_id = {}
  
      logging_operations = RunOperations(logging_futures)
      if synchronous:
          logging_operations.await_completion()
      return logging_operations
  
imports: import logging
  import os
  from collections import namedtuple
  from concurrent.futures import ThreadPoolExecutor
  from itertools import zip_longest
  from typing import Any, Dict, List, Optional, Union
  from mlflow.entities import Metric, Param, RunTag
  from mlflow.entities.dataset_input import DatasetInput
  from mlflow.exceptions import MlflowException
  from mlflow.tracking.client import MlflowClient
  from mlflow.utils import _truncate_dict, chunk_list
  from mlflow.utils.time import get_current_time_millis
  from mlflow.utils.validation import MAX_DATASETS_PER_BATCH, MAX_ENTITIES_PER_BATCH, MAX_ENTITY_KEY_LENGTH, MAX_METRICS_PER_BATCH, MAX_PARAM_VAL_LENGTH, MAX_PARAMS_TAGS_PER_BATCH, MAX_TAG_VAL_LENGTH
label_desc: Use image node_id 10 for calling the MlflowAutologgingQueueingClient obj's underlying member method code with example usage: obj.flush(synchronous) and returns: logging_operations