parameters: (
      inputs, units,
      activation=None,
      use_bias=True,
      kernel_initializer=None,
      bias_initializer=init_ops.zeros_initializer(),
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      trainable=True,
      name=None,
      reuse=None)
return_statement: return layer.apply(inputs)
docstring: Functional interface for the densely-connected layer.
  
    This layer implements the operation:
    `outputs = activation(inputs.kernel + bias)`
    Where `activation` is the activation function passed as the `activation`
    argument (if not `None`), `kernel` is a weights matrix created by the layer,
    and `bias` is a bias vector created by the layer
    (only if `use_bias` is `True`).
  
    Note: if the `inputs` tensor has a rank greater than 2, then it is
    flattened prior to the initial matrix multiply by `kernel`.
  
    Arguments:
      inputs: Tensor input.
      units: Integer or Long, dimensionality of the output space.
      activation: Activation function (callable). Set it to None to maintain a
        linear activation.
      use_bias: Boolean, whether the layer uses a bias.
      kernel_initializer: Initializer function for the weight matrix.
      bias_initializer: Initializer function for the bias.
      kernel_regularizer: Regularizer function for the weight matrix.
      bias_regularizer: Regularizer function for the bias.
      activity_regularizer: Regularizer function for the output.
      trainable: Boolean, if `True` also add variables to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      name: String, the name of the layer.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.
  
    Returns:
      Output tensor.
docstring_summary: Functional interface for the densely-connected layer.
docstring_tokens: Functional interface for the densely - connected layer .
function: def dense(
      inputs, units,
      activation=None,
      use_bias=True,
      kernel_initializer=None,
      bias_initializer=init_ops.zeros_initializer(),
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      trainable=True,
      name=None,
      reuse=None):
    """Functional interface for the densely-connected layer.
  
    This layer implements the operation:
    `outputs = activation(inputs.kernel + bias)`
    Where `activation` is the activation function passed as the `activation`
    argument (if not `None`), `kernel` is a weights matrix created by the layer,
    and `bias` is a bias vector created by the layer
    (only if `use_bias` is `True`).
  
    Note: if the `inputs` tensor has a rank greater than 2, then it is
    flattened prior to the initial matrix multiply by `kernel`.
  
    Arguments:
      inputs: Tensor input.
      units: Integer or Long, dimensionality of the output space.
      activation: Activation function (callable). Set it to None to maintain a
        linear activation.
      use_bias: Boolean, whether the layer uses a bias.
      kernel_initializer: Initializer function for the weight matrix.
      bias_initializer: Initializer function for the bias.
      kernel_regularizer: Regularizer function for the weight matrix.
      bias_regularizer: Regularizer function for the bias.
      activity_regularizer: Regularizer function for the output.
      trainable: Boolean, if `True` also add variables to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      name: String, the name of the layer.
      reuse: Boolean, whether to reuse the weights of a previous layer
        by the same name.
  
    Returns:
      Output tensor.
    """
    layer = Dense(units,
                  activation=activation,
                  use_bias=use_bias,
                  kernel_initializer=kernel_initializer,
                  bias_initializer=bias_initializer,
                  kernel_regularizer=kernel_regularizer,
                  bias_regularizer=bias_regularizer,
                  activity_regularizer=activity_regularizer,
                  trainable=trainable,
                  name=name,
                  dtype=inputs.dtype.base_dtype,
                  _scope=name,
                  _reuse=reuse)
    return layer.apply(inputs)
function_tokens: def dense ( inputs , units , activation = None , use_bias = True , kernel_initializer = None , bias_initializer = init_ops . zeros_initializer ( ) , kernel_regularizer = None , bias_regularizer = None , activity_regularizer = None , trainable = True , name = None , reuse = None ) : layer = Dense ( units , activation = activation , use_bias = use_bias , kernel_initializer = kernel_initializer , bias_initializer = bias_initializer , kernel_regularizer = kernel_regularizer , bias_regularizer = bias_regularizer , activity_regularizer = activity_regularizer , trainable = trainable , name = name , dtype = inputs . dtype . base_dtype , _scope = name , _reuse = reuse ) return layer . apply ( inputs )