code: 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,
      kernel_constraint=None,
      bias_constraint=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`).
  
      Args:
        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.
          If `None` (default), weights are initialized using the default
          initializer used by `tf.compat.v1.get_variable`.
        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.
        kernel_constraint: An optional projection function to be applied to the
            kernel after being updated by an `Optimizer` (e.g. used to implement
            norm constraints or value constraints for layer weights). The function
            must take as input the unprojected variable and must return the
            projected variable (which must have the same shape). Constraints are
            not safe to use when doing asynchronous distributed training.
        bias_constraint: An optional projection function to be applied to the
            bias after being updated by an `Optimizer`.
        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 the same shape as `inputs` except the last dimension is of
        size `units`.
  
      Raises:
        ValueError: if eager execution is enabled.
      """
      warnings.warn(
          "`tf.layers.dense` is deprecated and "
          "will be removed in a future version. "
          "Please use `tf.keras.layers.Dense` instead."
      )
      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,
          kernel_constraint=kernel_constraint,
          bias_constraint=bias_constraint,
          trainable=trainable,
          name=name,
          _scope=name,
          _reuse=reuse,
      )
      return layer.apply(inputs)
  
file_path: tensorflow-upstream/tensorflow/python/keras/legacy_tf_layers/core.py
imports: import warnings
  from tensorflow.python.keras import layers
  from tensorflow.python.keras.legacy_tf_layers import base
  from tensorflow.python.ops import init_ops
label_desc: Use image node_id 1 for calling a global function with example usage: dense(inputs, units, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, reuse) and returns: layer