parameters: (inputs,
               activation_fn=None,
               initializer=init_ops.zeros_initializer,
               regularizer=None,
               reuse=None,
               variables_collections=None,
               outputs_collections=None,
               trainable=True,
               scope=None)
docstring: Adds a bias to the inputs.
  
    Can be used as a normalizer function for conv2d and fully_connected.
  
    Args:
      inputs: a tensor of with at least rank 2 and value for the last dimension,
        e.g. `[batch_size, depth]`, `[None, None, None, depth]`.
      activation_fn: Optional activation function.
      initializer: An initializer for the bias, defaults to 0.
      regularizer: A regularizer like the result of
        `l1_regularizer` or `l2_regularizer`.
      reuse: whether or not the layer and its variables should be reused. To be
        able to reuse the layer scope must be given.
      variables_collections: optional collections for the variables.
      outputs_collections: collections to add the outputs.
      trainable: If `True` also add variables to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
      scope: Optional scope for variable_op_scope.
  
    Returns:
      a tensor representing the result of adding biases to the inputs.
docstring_summary: Adds a bias to the inputs.
docstring_tokens: Adds a bias to the inputs .
function: def bias_add(inputs,
               activation_fn=None,
               initializer=init_ops.zeros_initializer,
               regularizer=None,
               reuse=None,
               variables_collections=None,
               outputs_collections=None,
               trainable=True,
               scope=None):
    """Adds a bias to the inputs.
  
    Can be used as a normalizer function for conv2d and fully_connected.
  
    Args:
      inputs: a tensor of with at least rank 2 and value for the last dimension,
        e.g. `[batch_size, depth]`, `[None, None, None, depth]`.
      activation_fn: Optional activation function.
      initializer: An initializer for the bias, defaults to 0.
      regularizer: A regularizer like the result of
        `l1_regularizer` or `l2_regularizer`.
      reuse: whether or not the layer and its variables should be reused. To be
        able to reuse the layer scope must be given.
      variables_collections: optional collections for the variables.
      outputs_collections: collections to add the outputs.
      trainable: If `True` also add variables to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
      scope: Optional scope for variable_op_scope.
  
    Returns:
      a tensor representing the result of adding biases to the inputs.
    """
    with variable_scope.variable_op_scope([inputs],
                                          scope, 'BiasAdd', reuse=reuse) as sc:
      inputs = ops.convert_to_tensor(inputs)
      dtype = inputs.dtype.base_dtype
      num_features = utils.last_dimension(inputs.get_shape(), min_rank=2)
      biases_collections = utils.get_variable_collections(variables_collections,
                                                          'biases')
      biases = variables.model_variable('biases',
                                        shape=[num_features,],
                                        dtype=dtype,
                                        initializer=initializer,
                                        regularizer=regularizer,
                                        collections=biases_collections,
                                        trainable=trainable)
      outputs = nn.bias_add(inputs, biases)
      if activation_fn:
        outputs = activation_fn(outputs)
      return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
function_tokens: def bias_add ( inputs , activation_fn = None , initializer = init_ops . zeros_initializer , regularizer = None , reuse = None , variables_collections = None , outputs_collections = None , trainable = True , scope = None ) : with variable_scope . variable_op_scope ( [ inputs ] , scope , 'BiasAdd' , reuse = reuse ) as sc : inputs = ops . convert_to_tensor ( inputs ) dtype = inputs . dtype . base_dtype num_features = utils . last_dimension ( inputs . get_shape ( ) , min_rank = 2 ) biases_collections = utils . get_variable_collections ( variables_collections , 'biases' ) biases = variables . model_variable ( 'biases' , shape = [ num_features , ] , dtype = dtype , initializer = initializer , regularizer = regularizer , collections = biases_collections , trainable = trainable ) outputs = nn . bias_add ( inputs , biases ) if activation_fn : outputs = activation_fn ( outputs ) return utils . collect_named_outputs ( outputs_collections , sc . name , outputs )