parameters: (file_pattern,
                          batch_size,
                          reader,
                          randomize_input=True,
                          num_epochs=None,
                          queue_capacity=10000,
                          num_threads=1,
                          read_batch_size=1,
                          parse_fn=None,
                          name=None,
                          seed=None)
return_statement: return examples
docstring: Adds operations to read, queue, batch `Example` protos.
  
    Given file pattern (or list of files), will setup a queue for file names,
    read `Example` proto using provided `reader`, use batch queue to create
    batches of examples of size `batch_size`.
  
    All queue runners are added to the queue runners collection, and may be
    started via `start_queue_runners`.
  
    All ops are added to the default graph.
  
    Use `parse_fn` if you need to do parsing / processing on single examples.
  
    Args:
      file_pattern: List of files or patterns of file paths containing
          `Example` records. See `tf.gfile.Glob` for pattern rules.
      batch_size: An int or scalar `Tensor` specifying the batch size to use.
      reader: A function or class that returns an object with
        `read` method, (filename tensor) -> (example tensor).
      randomize_input: Whether the input should be randomized.
      num_epochs: Integer specifying the number of times to read through the
        dataset. If `None`, cycles through the dataset forever.
        NOTE - If specified, creates a variable that must be initialized, so call
        `tf.local_variables_initializer()` and run the op in a session.
      queue_capacity: Capacity for input queue.
      num_threads: The number of threads enqueuing examples. In order to have
        predictable and repeatable order of reading and enqueueing, such as in
        prediction and evaluation mode, `num_threads` should be 1.
      read_batch_size: An int or scalar `Tensor` specifying the number of
        records to read at once.
      parse_fn: Parsing function, takes `Example` Tensor returns parsed
        representation. If `None`, no parsing is done.
      name: Name of resulting op.
      seed: An integer (optional). Seed used if randomize_input == True.
  
    Returns:
      String `Tensor` of batched `Example` proto.
  
    Raises:
      ValueError: for invalid inputs.
docstring_summary: Adds operations to read, queue, batch `Example` protos.
docstring_tokens: Adds operations to read queue batch Example protos .
function: def read_batch_examples(file_pattern,
                          batch_size,
                          reader,
                          randomize_input=True,
                          num_epochs=None,
                          queue_capacity=10000,
                          num_threads=1,
                          read_batch_size=1,
                          parse_fn=None,
                          name=None,
                          seed=None):
    """Adds operations to read, queue, batch `Example` protos.
  
    Given file pattern (or list of files), will setup a queue for file names,
    read `Example` proto using provided `reader`, use batch queue to create
    batches of examples of size `batch_size`.
  
    All queue runners are added to the queue runners collection, and may be
    started via `start_queue_runners`.
  
    All ops are added to the default graph.
  
    Use `parse_fn` if you need to do parsing / processing on single examples.
  
    Args:
      file_pattern: List of files or patterns of file paths containing
          `Example` records. See `tf.gfile.Glob` for pattern rules.
      batch_size: An int or scalar `Tensor` specifying the batch size to use.
      reader: A function or class that returns an object with
        `read` method, (filename tensor) -> (example tensor).
      randomize_input: Whether the input should be randomized.
      num_epochs: Integer specifying the number of times to read through the
        dataset. If `None`, cycles through the dataset forever.
        NOTE - If specified, creates a variable that must be initialized, so call
        `tf.local_variables_initializer()` and run the op in a session.
      queue_capacity: Capacity for input queue.
      num_threads: The number of threads enqueuing examples. In order to have
        predictable and repeatable order of reading and enqueueing, such as in
        prediction and evaluation mode, `num_threads` should be 1.
      read_batch_size: An int or scalar `Tensor` specifying the number of
        records to read at once.
      parse_fn: Parsing function, takes `Example` Tensor returns parsed
        representation. If `None`, no parsing is done.
      name: Name of resulting op.
      seed: An integer (optional). Seed used if randomize_input == True.
  
    Returns:
      String `Tensor` of batched `Example` proto.
  
    Raises:
      ValueError: for invalid inputs.
    """
    _, examples = read_keyed_batch_examples(
        file_pattern=file_pattern,
        batch_size=batch_size,
        reader=reader,
        randomize_input=randomize_input,
        num_epochs=num_epochs,
        queue_capacity=queue_capacity,
        num_threads=num_threads,
        read_batch_size=read_batch_size,
        parse_fn=parse_fn,
        name=name,
        seed=seed)
    return examples
function_tokens: def read_batch_examples ( file_pattern , batch_size , reader , randomize_input = True , num_epochs = None , queue_capacity = 10000 , num_threads = 1 , read_batch_size = 1 , parse_fn = None , name = None , seed = None ) : _ , examples = read_keyed_batch_examples ( file_pattern = file_pattern , batch_size = batch_size , reader = reader , randomize_input = randomize_input , num_epochs = num_epochs , queue_capacity = queue_capacity , num_threads = num_threads , read_batch_size = read_batch_size , parse_fn = parse_fn , name = name , seed = seed ) return examples