class_docstr: Logs to a tf.summary created in a given logdir.
  
  If multiple TFSummaryLogger are created with the same logdir, results will be
  categorized by labels.
class_docstr_tok: ["Logs","to","a","tf.summary","created","in","a","given","logdir",".","If","multiple","TFSummaryLogger","are","created","with","the","same","logdir",",","results","will","be","categorized","by","labels","."]
docstr: Initializes the logger.
  
  Args:
    logdir: directory to which we should log files.
    label: label string to use when logging. Default to 'Logs'.
    steps_key: key to use for steps. Must be in the values passed to write.
docstr_tok: ["Initializes","the","logger",".","Args",":","logdir",":","directory","to","which","we","should","log","files",".","label",":","label","string","to","use","when","logging",".","Default","to","'Logs","'",".","steps_key",":","key","to","use","for","steps",".","Must","be","in","the","values","passed","to","write","."]
code: def __init__(
      self,
      logdir: str,
      label: str = "Logs",
      steps_key: Optional[str] = None,
  ):
      """Initializes the logger.
  
      Args:
        logdir: directory to which we should log files.
        label: label string to use when logging. Default to 'Logs'.
        steps_key: key to use for steps. Must be in the values passed to write.
      """
      self._time = time.time()
      self.label = label
      self._iter = 0
      self.summary = tf.summary.create_file_writer(logdir)
      self._steps_key = steps_key
  
file_path: acme/acme/utils/loggers/tf_summary.py
imports: import time
  from typing import Optional
  from absl import logging
  from acme.utils.loggers import base
  import tensorflow
label_desc: Use image node_id 1 to create a new TFSummaryLogger object from inherited base classes: base with example: obj = TFSummaryLogger(logdir, label, steps_key)