class_docstr: Python iterator for TF Records based on PyRecordReader.
class_docstr_tok: ["Python","iterator","for","TF","Records","based","on","PyRecordReader","."]
docstr: Constructs a _PyRecordReaderIterator for the given file path.
  
  Args:
    py_record_reader_new: pywrap_tensorflow.PyRecordReader_New
    file_path: file path of the tfrecord file to read
docstr_tok: ["Constructs","a","_PyRecordReaderIterator","for","the","given","file","path",".","Args",":","py_record_reader_new",":","pywrap_tensorflow.PyRecordReader_New","file_path",":","file","path","of","the","tfrecord","file","to","read"]
code: def __init__(self, py_record_reader_new, file_path):
      """Constructs a _PyRecordReaderIterator for the given file path.
  
      Args:
        py_record_reader_new: pywrap_tensorflow.PyRecordReader_New
        file_path: file path of the tfrecord file to read
      """
      with tf.compat.v1.errors.raise_exception_on_not_ok_status() as status:
          self._reader = py_record_reader_new(
              tf.compat.as_bytes(file_path),
              0,
              tf.compat.as_bytes(""),
              status,
          )
      if not self._reader:
          raise IOError(
              "Failed to open a record reader pointing to %s"
              % file_path
          )
  
imports: import contextlib
  from tensorboard import data_compat
  from tensorboard import dataclass_compat
  from tensorboard.compat import tf
  from tensorboard.compat.proto import event_pb2
  from tensorboard.util import platform_util
  from tensorboard.util import tb_logging
label_desc: Use image node_id 1 to create a new _PyRecordReaderIterator object  with example: obj = _PyRecordReaderIterator(py_record_reader_new, file_path)