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: Enqueues a CreateRun operation with the specified attributes, returning a `PendingRunId`
  instance that can be used as input to other client logging APIs (e.g. `log_metrics`,
  `log_params`, ...).
  
  Returns:
      A `PendingRunId` that can be passed as the `run_id` parameter to other client
      logging APIs, such as `log_params` and `log_metrics`.
docstr_tok: ["Enqueues","a","CreateRun","operation","with","the","specified","attributes",",","returning","a","`","PendingRunId","`","instance","that","can","be","used","as","input","to","other","client","logging","APIs","(","e.g",".","`","log_metrics","`",",","`","log_params","`",",","...",")",".","Returns",":","A","`","PendingRunId","`","that","can","be","passed","as","the","`","run_id","`","parameter","to","other","client","logging","APIs",",","such","as","`","log_params","`","and","`","log_metrics","`","."]
code: def create_run(
      self,
      experiment_id: str,
      start_time: Optional[int] = None,
      tags: Optional[Dict[str, Any]] = None,
      run_name: Optional[str] = None,
  ) -> PendingRunId:
      """
      Enqueues a CreateRun operation with the specified attributes, returning a `PendingRunId`
      instance that can be used as input to other client logging APIs (e.g. `log_metrics`,
      `log_params`, ...).
  
      Returns:
          A `PendingRunId` that can be passed as the `run_id` parameter to other client
          logging APIs, such as `log_params` and `log_metrics`.
      """
      tags = tags or {}
      tags = _truncate_dict(
          tags,
          max_key_length=MAX_ENTITY_KEY_LENGTH,
          max_value_length=MAX_TAG_VAL_LENGTH,
      )
      run_id = PendingRunId()
      self._get_pending_operations(run_id).enqueue(
          create_run=_PendingCreateRun(
              experiment_id=experiment_id,
              start_time=start_time,
              tags=[
                  RunTag(key, str(value)) for key, value in tags.items()
              ],
              run_name=run_name,
          )
      )
      return run_id
  
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 4 for calling the MlflowAutologgingQueueingClient obj's underlying member method code with example usage: obj.create_run(experiment_id, start_time, tags, run_name) and returns: run_id