class_docstr: Starts a new tracked run that logs ML model-building metadata to neptune.ai.
  
  You can log metadata by assigning it to the initialized Run object:
  
  ```
  run = neptune.init_run()
  run["your/structure"] = some_metadata
  ```
  
  Examples of metadata you can log: metrics, losses, scores, artifact versions, images, predictions,
  model weights, parameters, checkpoints, and interactive visualizations.
  
  By default, the run automatically tracks hardware consumption, stdout/stderr, source code, and Git information.
  If you're using Neptune in an interactive session, however, some background monitoring needs to be enabled
  explicitly.
  
  If you provide the ID of an existing run, that run is resumed and no new run is created. You may resume a run
  either to log more metadata or to fetch metadata from it.
  
  The run ends either when its `stop()` method is called or when the script finishes execution.
  
  You can also use the Run object as a context manager (see examples).
  
  Args:
      project: Name of the project where the run should go, in the form `workspace-name/project_name`.
          If left empty, the value of the NEPTUNE_PROJECT environment variable is used.
      api_token: User's API token.
          If left empty, the value of the NEPTUNE_API_TOKEN environment variable is used (recommended).
      with_id: If you want to resume a run, pass the identifier of an existing run. For example, "SAN-1".
          If left empty, a new run is created.
      custom_run_id: A unique identifier to be used when running Neptune in distributed training jobs.
          Make sure to use the same identifier throughout the whole pipeline execution.
      mode: Connection mode in which the tracking will work.
          If left empty, the value of the NEPTUNE_MODE environment variable is used.
          If no value was set for the environment variable, "async" is used by default.
          Possible values are `async`, `sync`, `offline`, `read-only`, and `debug`.
      name: Custom name for the run. You can add it as a column in the runs table ("sys/name").
          You can also edit the name in the app: Open the run menu and access the run information.
      description:  Custom description of the run. You can add it as a column in the runs table
          ("sys/description").
          You can also edit the description in the app: Open the run menu and access the run information.
      tags: Tags of the run as a list of strings.
          You can edit the tags through the "sys/tags" field or in the app (run menu -> information).
          You can also select multiple runs and manage their tags as a single action.
      source_files: List of source files to be uploaded.
          Uploaded source files are displayed in the "Source code" dashboard.
          To not upload anything, pass an empty list (`[]`).
          Unix style pathname pattern expansion is supported. For example, you can pass `*.py` to upload
          all Python files from the current directory.
          If None is passed, the Python file from which the run was created will be uploaded.
      capture_stdout: Whether to log the stdout of the run.
          Defaults to `False` in interactive sessions and `True` otherwise.
          The data is logged under the monitoring namespace (see the `monitoring_namespace` parameter).
      capture_stderr: Whether to log the stderr of the run.
          Defaults to `False` in interactive sessions and `True` otherwise.
          The data is logged under the monitoring namespace (see the `monitoring_namespace` parameter).
      capture_hardware_metrics: Whether to send hardware monitoring logs (CPU, GPU, and memory utilization).
          Defaults to `False` in interactive sessions and `True` otherwise.
          The data is logged under the monitoring namespace (see the `monitoring_namespace` parameter).
      fail_on_exception: Whether to register an uncaught exception handler to this process and,
          in case of an exception, set the "sys/failed" field of the run to `True`.
          An exception is always logged.
      monitoring_namespace: Namespace inside which all hardware monitoring logs are stored.
          Defaults to "monitoring/<hash>", where the hash is generated based on environment information,
          to ensure that it's unique for each process.
      flush_period: In the asynchronous (default) connection mode, how often disk flushing is triggered
          (in seconds).
      proxies: Argument passed to HTTP calls made via the Requests library, as dictionary of strings.
          For more information about proxies, see the Requests documentation.
      capture_traceback: Whether to log the traceback of the run in case of an exception.
          The tracked metadata is stored in the "<monitoring_namespace>/traceback" namespace (see the
          `monitoring_namespace` parameter).
      git_ref: GitRef object containing information about the Git repository path.
          If None, Neptune looks for a repository in the path of the script that is executed.
          To specify a different location, set to GitRef(repository_path="path/to/repo").
          To turn off Git tracking for the run, set to False or GitRef.DISABLED.
      dependencies: If you pass `"infer"`, Neptune logs dependencies installed in the current environment.
          You can also pass a path to your dependency file directly.
          If left empty, no dependencies are tracked.
      async_lag_callback: Custom callback which is called if the lag between a queued operation and its
          synchronization with the server exceeds the duration defined by `async_lag_threshold`. The callback
          should take a Run object as the argument and can contain any custom code, such as calling `stop()` on
          the object.
          Note: Instead of using this argument, you can use Neptune's default callback by setting the
          `NEPTUNE_ENABLE_DEFAULT_ASYNC_LAG_CALLBACK` environment variable to `TRUE`.
      async_lag_threshold: In seconds, duration between the queueing and synchronization of an operation.
          If a lag callback (default callback enabled via environment variable or custom callback passed to the
          `async_lag_callback` argument) is enabled, the callback is called when this duration is exceeded.
      async_no_progress_callback: Custom callback which is called if there has been no synchronization progress
          whatsoever for the duration defined by `async_no_progress_threshold`. The callback
          should take a Run object as the argument and can contain any custom code, such as calling `stop()` on
          the object.
          Note: Instead of using this argument, you can use Neptune's default callback by setting the
          `NEPTUNE_ENABLE_DEFAULT_ASYNC_NO_PROGRESS_CALLBACK` environment variable to `TRUE`.
      async_no_progress_threshold: In seconds, for how long there has been no synchronization progress since the
          object was initialized. If a no-progress callback (default callback enabled via environment variable or
          custom callback passed to the `async_no_progress_callback` argument) is enabled, the callback is called
          when this duration is exceeded.
  
  Returns:
      Run object that is used to manage the tracked run and log metadata to it.
  
  Examples:
  
      Creating a new run:
  
      >>> import neptune
  
      >>> # Minimal invoke
      ... # (creates a run in the project specified by the NEPTUNE_PROJECT environment variable)
      ... run = neptune.init_run()
  
      >>> # Or initialize with the constructor
      ... run = Run(project="ml-team/classification")
  
      >>> # Create a run with a name and description, with no sources files or Git info tracked:
      >>> run = neptune.init_run(
      ...     name="neural-net-mnist",
      ...     description="neural net trained on MNIST",
      ...     source_files=[],
      ...     git_ref=False,
      ... )
  
      >>> # Log all .py files from all subdirectories, excluding hidden files
      ... run = neptune.init_run(source_files="**/*.py")
  
      >>> # Log all files and directories in the current working directory, excluding hidden files
      ... run = neptune.init_run(source_files="*")
  
      >>> # Larger example
      ... run = neptune.init_run(
      ...     project="ml-team/classification",
      ...     name="first-pytorch-ever",
      ...     description="Longer description of the run goes here",
      ...     tags=["tags", "go-here", "as-list-of-strings"],
      ...     source_files=["training_with_pytorch.py", "net.py"],
      ...     dependencies="infer",
      ...     capture_stderr=False,
      ...     git_ref=GitRef(repository_path="/Users/Jackie/repos/cls_project"),
      ... )
  
      Connecting to an existing run:
  
      >>> # Resume logging to an existing run with the ID "SAN-3"
      ... run = neptune.init_run(with_id="SAN-3")
      ... run["parameters/lr"] = 0.1  # modify or add metadata
  
      >>> # Initialize an existing run in read-only mode (logging new data is not possible, only fetching)
      ... run = neptune.init_run(with_id="SAN-4", mode="read-only")
      ... learning_rate = run["parameters/lr"].fetch()
  
      Using the Run object as context manager:
  
      >>> with Run() as run:
      ...     run["metric"].append(value)
  
  For more, see the docs:
      Initializing a run:
          https://docs.neptune.ai/api/neptune#init_run
      Run class reference:
          https://docs.neptune.ai/api/run/
      Essential logging methods:
          https://docs.neptune.ai/logging/methods/
      Resuming a run:
          https://docs.neptune.ai/logging/to_existing_object/
      Setting a custom run ID:
          https://docs.neptune.ai/logging/custom_run_id/
      Logging to multiple runs at once:
          https://docs.neptune.ai/logging/to_multiple_objects/
      Accessing the run from multiple places:
          https://docs.neptune.ai/logging/from_multiple_places/
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code: def __init__(
      self,
      with_id: Optional[str] = None,
      *,
      project: Optional[str] = None,
      api_token: Optional[str] = None,
      custom_run_id: Optional[str] = None,
      mode: Optional[
          Literal["async", "sync", "offline", "read-only", "debug"]
      ] = None,
      name: Optional[str] = None,
      description: Optional[str] = None,
      tags: Optional[Union[List[str], str]] = None,
      source_files: Optional[Union[List[str], str]] = None,
      capture_stdout: Optional[bool] = None,
      capture_stderr: Optional[bool] = None,
      capture_hardware_metrics: Optional[bool] = None,
      fail_on_exception: bool = True,
      monitoring_namespace: Optional[str] = None,
      flush_period: float = DEFAULT_FLUSH_PERIOD,
      proxies: Optional[dict] = None,
      capture_traceback: bool = True,
      git_ref: Optional[Union[GitRef, GitRefDisabled, bool]] = None,
      dependencies: Optional[Union[str, os.PathLike]] = None,
      async_lag_callback: Optional[NeptuneObjectCallback] = None,
      async_lag_threshold: float = ASYNC_LAG_THRESHOLD,
      async_no_progress_callback: Optional[
          NeptuneObjectCallback
      ] = None,
      async_no_progress_threshold: float = ASYNC_NO_PROGRESS_THRESHOLD,
      **kwargs,
  ):
      check_for_extra_kwargs("Run", kwargs)
  
      verify_type("with_id", with_id, (str, type(None)))
      verify_type("project", project, (str, type(None)))
      verify_type("custom_run_id", custom_run_id, (str, type(None)))
      verify_type("mode", mode, (str, type(None)))
      verify_type("name", name, (str, type(None)))
      verify_type("description", description, (str, type(None)))
      verify_type("capture_stdout", capture_stdout, (bool, type(None)))
      verify_type("capture_stderr", capture_stderr, (bool, type(None)))
      verify_type(
          "capture_hardware_metrics",
          capture_hardware_metrics,
          (bool, type(None)),
      )
      verify_type("fail_on_exception", fail_on_exception, bool)
      verify_type(
          "monitoring_namespace",
          monitoring_namespace,
          (str, type(None)),
      )
      verify_type("capture_traceback", capture_traceback, bool)
      verify_type("git_ref", git_ref, (GitRef, str, bool, type(None)))
      verify_type(
          "dependencies", dependencies, (str, os.PathLike, type(None))
      )
  
      if tags is not None:
          if isinstance(tags, str):
              tags = [tags]
          else:
              verify_collection_type("tags", tags, str)
      if source_files is not None:
          if isinstance(source_files, str):
              source_files = [source_files]
          else:
              verify_collection_type("source_files", source_files, str)
  
      self._with_id: Optional[str] = with_id
      self._name: Optional[str] = (
          DEFAULT_NAME if with_id is None and name is None else name
      )
      self._description: Optional[str] = (
          "" if with_id is None and description is None else description
      )
      self._custom_run_id: Optional[str] = custom_run_id or os.getenv(
          CUSTOM_RUN_ID_ENV_NAME
      )
      self._hostname: str = get_hostname()
      self._pid: int = os.getpid()
      self._tid: int = threading.get_ident()
      self._tags: Optional[List[str]] = tags
      self._source_files: Optional[List[str]] = source_files
      self._fail_on_exception: bool = fail_on_exception
      self._capture_traceback: bool = capture_traceback
  
      if type(git_ref) is bool:
          git_ref = GitRef() if git_ref else GitRef.DISABLED
  
      self._git_ref: Optional[GitRef, GitRefDisabled] = (
          git_ref or GitRef()
      )
      self._dependencies: Optional[str, os.PathLike] = dependencies
  
      self._monitoring_namespace: str = (
          monitoring_namespace
          or os.getenv(MONITORING_NAMESPACE)
          or generate_monitoring_namespace(
              self._hostname, self._pid, self._tid
          )
      )
  
      # for backward compatibility imports
      mode = Mode(
          mode or os.getenv(CONNECTION_MODE) or Mode.ASYNC.value
      )
  
      self._stdout_path: str = "{}/stdout".format(
          self._monitoring_namespace
      )
      self._capture_stdout: bool = capture_stdout
      if capture_stdout is None:
          self._capture_stdout = capture_only_if_non_interactive(
              mode=mode
          )
  
      self._stderr_path: str = "{}/stderr".format(
          self._monitoring_namespace
      )
      self._capture_stderr: bool = capture_stderr
      if capture_stderr is None:
          self._capture_stderr = capture_only_if_non_interactive(
              mode=mode
          )
  
      self._capture_hardware_metrics: bool = capture_hardware_metrics
      if capture_hardware_metrics is None:
          self._capture_hardware_metrics = (
              capture_only_if_non_interactive(mode=mode)
          )
  
      if with_id and custom_run_id:
          raise NeptuneRunResumeAndCustomIdCollision()
  
      if mode == Mode.OFFLINE or mode == Mode.DEBUG:
          project = OFFLINE_PROJECT_QUALIFIED_NAME
  
      super().__init__(
          project=project,
          api_token=api_token,
          mode=mode,
          flush_period=flush_period,
          proxies=proxies,
          async_lag_callback=async_lag_callback,
          async_lag_threshold=async_lag_threshold,
          async_no_progress_callback=async_no_progress_callback,
          async_no_progress_threshold=async_no_progress_threshold,
      )
  
imports: import os
  import threading
  from platform import node
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from typing_extensions import Literal
  from neptune.attributes.constants import SYSTEM_DESCRIPTION_ATTRIBUTE_PATH, SYSTEM_FAILED_ATTRIBUTE_PATH, SYSTEM_HOSTNAME_ATTRIBUTE_PATH, SYSTEM_NAME_ATTRIBUTE_PATH, SYSTEM_TAGS_ATTRIBUTE_PATH
  from neptune.common.warnings import NeptuneWarning, warn_once
  from neptune.envs import CONNECTION_MODE, CUSTOM_RUN_ID_ENV_NAME, MONITORING_NAMESPACE, NEPTUNE_NOTEBOOK_ID, NEPTUNE_NOTEBOOK_PATH
  from neptune.exceptions import InactiveRunException, NeedExistingRunForReadOnlyMode, NeptunePossibleLegacyUsageException, NeptuneRunResumeAndCustomIdCollision
  from neptune.internal.backends.api_model import ApiExperiment
  from neptune.internal.backends.neptune_backend import NeptuneBackend
  from neptune.internal.container_type import ContainerType
  from neptune.internal.hardware.hardware_metric_reporting_job import HardwareMetricReportingJob
  from neptune.internal.id_formats import QualifiedName
  from neptune.internal.init.parameters import ASYNC_LAG_THRESHOLD, ASYNC_NO_PROGRESS_THRESHOLD, DEFAULT_FLUSH_PERIOD, DEFAULT_NAME, OFFLINE_PROJECT_QUALIFIED_NAME
  from neptune.internal.notebooks.notebooks import create_checkpoint
  from neptune.internal.state import ContainerState
  from neptune.internal.streams.std_capture_background_job import StderrCaptureBackgroundJob, StdoutCaptureBackgroundJob
  from neptune.internal.utils import verify_collection_type, verify_type
  from neptune.internal.utils.dependency_tracking import FileDependenciesStrategy, InferDependenciesStrategy
  from neptune.internal.utils.git import to_git_info, track_uncommitted_changes
  from neptune.internal.utils.hashing import generate_hash
  from neptune.internal.utils.limits import custom_run_id_exceeds_length
  from neptune.internal.utils.ping_background_job import PingBackgroundJob
  from neptune.internal.utils.runningmode import in_interactive, in_notebook
  from neptune.internal.utils.source_code import upload_source_code
  from neptune.internal.utils.traceback_job import TracebackJob
  from neptune.internal.websockets.websocket_signals_background_job import WebsocketSignalsBackgroundJob
  from neptune.metadata_containers import MetadataContainer
  from neptune.metadata_containers.abstract import NeptuneObjectCallback
  from neptune.types import GitRef, StringSeries
  from neptune.types.atoms.git_ref import GitRefDisabled
  from neptune.types.mode import Mode
label_desc: Use image node_id 1 to create a new Run object from inherited base classes: MetadataContainer with example: obj = Run(with_id)