class_docstr: An internal class to track the lifetime of a Tensor.
class_docstr_tok: ["An","internal","class","to","track","the","lifetime","of","a","Tensor","."]
docstr: Creates an object to track tensor references.
  
  This class is not thread safe and is intended only for internal use by
  the 'Timeline' class in this file.
  
  Args:
    name:  The name of the Tensor as a string.
    object_id:  Chrome Trace object identifier assigned for this Tensor.
    timestamp:  The creation timestamp of this event as a long integer.
    pid:  Process identifier of the associated device, as an integer.
    allocator:  Name of the allocator used to create the Tensor.
    num_bytes:  Number of bytes allocated (long integer).
  
  Returns:
    A 'TensorTracker' object.
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code: def __init__(
      self,
      name: str,
      object_id: int,
      timestamp: int,
      pid: int,
      allocator: str,
      num_bytes: int,
  ) -> None:
      """Creates an object to track tensor references.
  
      This class is not thread safe and is intended only for internal use by
      the 'Timeline' class in this file.
  
      Args:
        name:  The name of the Tensor as a string.
        object_id:  Chrome Trace object identifier assigned for this Tensor.
        timestamp:  The creation timestamp of this event as a long integer.
        pid:  Process identifier of the associated device, as an integer.
        allocator:  Name of the allocator used to create the Tensor.
        num_bytes:  Number of bytes allocated (long integer).
  
      Returns:
        A 'TensorTracker' object.
      """
      self._name = name
      self._pid = pid
      self._object_id = object_id
      self._create_time = timestamp
      self._allocator = allocator
      self._num_bytes = num_bytes
      self._ref_times = []
      self._unref_times = []
  
imports: import collections
  import copy
  import json
  import re
  from typing import Any, Dict, List, Optional, Tuple, Union
  from tensorflow.core.framework import step_stats_pb2
  from tensorflow.python.platform import build_info
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 1 to create a new _TensorTracker object from inherited base classes: object with example: obj = _TensorTracker(name, object_id, timestamp, pid, allocator, num_bytes)