parameters: (cls,
                         ordered_dict_of_arrays,
                         num_threads=None,
                         enqueue_size=None,
                         batch_size=None,
                         queue_capacity=None,
                         min_after_dequeue=None,
                         shuffle=True,
                         seed=None,
                         data_name="numpy_data")
return_statement: return dataframe
docstring: Creates a `tf.learn.DataFrame` from an `OrderedDict` of `numpy.ndarray`.
  
      The returned `DataFrame` contains a column for each key of the dict plus an
      extra 'index' column. The 'index' column contains the row number. Each of
      the other columns contains a row from the corresponding array.
  
      Args:
        ordered_dict_of_arrays: `OrderedDict` of `numpy.ndarray` that serves as a
            data source.
        num_threads: the number of threads to use for enqueueing.
        enqueue_size: the number of rows to enqueue per step.
        batch_size: desired batch size.
        queue_capacity: capacity of the queue that will store parsed `Example`s
        min_after_dequeue: minimum number of elements that can be left by a
          dequeue operation. Only used if `shuffle` is true.
        shuffle: whether records should be shuffled. Defaults to true.
        seed: passed to random shuffle operations. Only used if `shuffle` is true.
        data_name: a scope name identifying the data.
  
      Returns:
        A `tf.learn.DataFrame` that contains batches drawn from the given arrays.
  
      Raises:
        ValueError: `ordered_dict_of_arrays` contains the reserved name 'index'.
docstring_summary: Creates a `tf.learn.DataFrame` from an `OrderedDict` of `numpy.ndarray`.
docstring_tokens: Creates a tf . learn . DataFrame from an OrderedDict of numpy . ndarray .
function: def from_ordereddict(cls,
                         ordered_dict_of_arrays,
                         num_threads=None,
                         enqueue_size=None,
                         batch_size=None,
                         queue_capacity=None,
                         min_after_dequeue=None,
                         shuffle=True,
                         seed=None,
                         data_name="numpy_data"):
      """Creates a `tf.learn.DataFrame` from an `OrderedDict` of `numpy.ndarray`.
  
      The returned `DataFrame` contains a column for each key of the dict plus an
      extra 'index' column. The 'index' column contains the row number. Each of
      the other columns contains a row from the corresponding array.
  
      Args:
        ordered_dict_of_arrays: `OrderedDict` of `numpy.ndarray` that serves as a
            data source.
        num_threads: the number of threads to use for enqueueing.
        enqueue_size: the number of rows to enqueue per step.
        batch_size: desired batch size.
        queue_capacity: capacity of the queue that will store parsed `Example`s
        min_after_dequeue: minimum number of elements that can be left by a
          dequeue operation. Only used if `shuffle` is true.
        shuffle: whether records should be shuffled. Defaults to true.
        seed: passed to random shuffle operations. Only used if `shuffle` is true.
        data_name: a scope name identifying the data.
  
      Returns:
        A `tf.learn.DataFrame` that contains batches drawn from the given arrays.
  
      Raises:
        ValueError: `ordered_dict_of_arrays` contains the reserved name 'index'.
      """
      numpy_source = in_memory_source.OrderedDictNumpySource(
          ordered_dict_of_arrays,
          num_threads=num_threads,
          enqueue_size=enqueue_size,
          batch_size=batch_size,
          queue_capacity=queue_capacity,
          shuffle=shuffle,
          min_after_dequeue=min_after_dequeue,
          seed=seed,
          data_name=data_name)
      dataframe = cls()
      dataframe.assign(**(numpy_source()._asdict()))
      return dataframe
function_tokens: def from_ordereddict ( cls , ordered_dict_of_arrays , num_threads = None , enqueue_size = None , batch_size = None , queue_capacity = None , min_after_dequeue = None , shuffle = True , seed = None , data_name = "numpy_data" ) : numpy_source = in_memory_source . OrderedDictNumpySource ( ordered_dict_of_arrays , num_threads = num_threads , enqueue_size = enqueue_size , batch_size = batch_size , queue_capacity = queue_capacity , shuffle = shuffle , min_after_dequeue = min_after_dequeue , seed = seed , data_name = data_name ) dataframe = cls ( ) dataframe . assign ( * * ( numpy_source ( ) . _asdict ( ) ) ) return dataframe