docs: def _apply_bias(inputs, outputs, channel_index, data_format, output_channels,
                  initializers, partitioners, regularizers):
    """Initialize and apply a bias to the outputs.
  
    Figures out the shape of the bias vector, initialize it, and applies it.
  
    Args:
      inputs: A Tensor of shape `data_format`.
      outputs: A Tensor of shape `data_format`.
      channel_index: The index of the channel dimension in `inputs`.
      data_format: Format of `inputs`.
      output_channels: Channel dimensionality for `outputs`.
      initializers: Optional dict containing ops to initialize the biases
        (with key 'b').
      partitioners: Optional dict containing partitioners to partition the
        biases (with key 'b').
      regularizers: Optional dict containing regularizers for the biases
        (with key 'b').
  
    Returns:
      b: The constructed bias variable.
      outputs: The `outputs` argument that has had a bias applied.
    """
    bias_shape = (output_channels,)
    if "b" not in initializers:
      initializers["b"] = create_bias_initializer(bias_shape,
                                                  dtype=inputs.dtype)
    b = tf.get_variable("b",
                        shape=bias_shape,
                        dtype=inputs.dtype,
                        initializer=initializers["b"],
                        partitioner=partitioners.get("b", None),
                        regularizer=regularizers.get("b", None))
  
    # tf.nn.bias_add only supports 2 data formats.
    if data_format in (DATA_FORMAT_NHWC, DATA_FORMAT_NCHW):
      # Supported as-is.
      outputs = tf.nn.bias_add(outputs, b, data_format=data_format)
    else:
      # Create our own bias vector.
      bias_correct_dim = [1] * len(data_format)
      bias_correct_dim[channel_index] = output_channels
      outputs += tf.reshape(b, bias_correct_dim)
  
    return b, outputs