class_docstr: File I/O wrappers without thread locking.
  
  The main roles of the `tf.io.gfile` module are:
  
  1. To provide an API that is close to Python's file I/O objects, and
  2. To provide an implementation based on TensorFlow's C++ FileSystem API.
  
  The C++ FileSystem API supports multiple file system implementations,
  including local files, Google Cloud Storage (using a `gs://` prefix, and
  HDFS (using an `hdfs://` prefix). TensorFlow exports these as `tf.io.gfile`,
  so that you can use these implementations for saving and loading checkpoints,
  writing to TensorBoard logs, and accessing training data (among other uses).
  However, if all your files are local, you can use the regular Python file
  API without any problem.
  
  *Note*: though similar to Python's I/O implementation, there are semantic
  differences to make `tf.io.gfile` more efficient for backing filesystems. For
  example, a write mode file will not be opened until the first write call to
  minimize RPC invocations in network filesystems.
  
  Once you obtain a `GFile` object, you can use it in most ways as you would any
  Python's file object:
  
  >>> with open("/tmp/x", "w") as f:
  ...   f.write("asdf")
  4
  >>> with tf.io.gfile.GFile("/tmp/x") as f:
  ...   f.read()
  'asdf'
  
  The difference is that you can specify URI schemes to use other filesystems
  (e.g., `gs://` for GCS, `s3://` for S3, etc.), if they are supported. Using
  `file://` as an example, we have:
  
  >>> with tf.io.gfile.GFile("file:///tmp/x", "w") as f:
  ...   f.write("qwert")
  ...   f.write("asdf")
  >>> tf.io.gfile.GFile("file:///tmp/x").read()
  'qwertasdf'
  
  You can also read all lines of a file directly:
  
  >>> with tf.io.gfile.GFile("file:///tmp/x", "w") as f:
  ...   f.write("asdf\n")
  ...   f.write("qwer\n")
  >>> tf.io.gfile.GFile("/tmp/x").readlines()
  ['asdf\n', 'qwer\n']
  
  You can iterate over the lines:
  
  >>> with tf.io.gfile.GFile("file:///tmp/x", "w") as f:
  ...   f.write("asdf\n")
  ...   f.write("qwer\n")
  >>> for line in tf.io.gfile.GFile("/tmp/x"):
  ...   print(line[:-1]) # removes the end of line character
  asdf
  qwer
  
  Random access read is possible if the underlying filesystem supports it:
  
  >>> with open("/tmp/x", "w") as f:
  ...   f.write("asdfqwer")
  >>> f = tf.io.gfile.GFile("/tmp/x")
  >>> f.read(3)
  'asd'
  >>> f.seek(4)
  >>> f.tell()
  4
  >>> f.read(3)
  'qwe'
  >>> f.tell()
  7
  >>> f.close()
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code: def __init__(self, name, mode="r"):
      super(GFile, self).__init__(name=name, mode=mode)
  
imports: from tensorflow.python.lib.io.file_io import copy
  from tensorflow.python.lib.io.file_io import create_dir
  from tensorflow.python.lib.io.file_io import delete_file
  from tensorflow.python.lib.io.file_io import delete_recursively
  from tensorflow.python.lib.io.file_io import file_exists
  from tensorflow.python.lib.io.file_io import FileIO
  from tensorflow.python.lib.io.file_io import get_matching_files
  from tensorflow.python.lib.io.file_io import is_directory
  from tensorflow.python.lib.io.file_io import list_directory
  from tensorflow.python.lib.io.file_io import recursive_create_dir
  from tensorflow.python.lib.io.file_io import rename
  from tensorflow.python.lib.io.file_io import stat
  from tensorflow.python.lib.io.file_io import walk
  from tensorflow.python.util.deprecation import deprecated
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new GFile object from inherited base classes: _FileIO with example: obj = GFile(name, mode)