class_docstr: A densely-connected NN layer.
  The most common input is 2D.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  units: The size of output dimension.
  kernel_initializer: String representation of the initialization method           for the weights of the layer.
        Default is 'glorot_uniform'.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  kernel_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the input weights matrices. Default is None.
  b_regularizer: An instance of [[Regularizer]], applied to the bias. Default is None.
  bias_regularizer: Whether to include a bias (i.e. make the layer affine rather than linear).
        Default is True.
  input_dim: Dimensionality of the input for 2D input. For nD input, you can alternatively
             specify 'input_shape' when using this layer as the first layer.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> dense = Dense(10, kernel_initializer="glorot_uniform", input_dim=8, name="dense1")
  creating: createZooKeras2Dense
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code: def __init__(
      self,
      units,
      kernel_initializer="glorot_uniform",
      bias_initializer="zero",
      activation=None,
      kernel_regularizer=None,
      bias_regularizer=None,
      use_bias=True,
      input_dim=None,
      input_shape=None,
      **kwargs,
  ):
      if input_dim:
          input_shape = (input_dim,)
      super(Dense, self).__init__(
          None,
          units,
          kernel_initializer,
          bias_initializer,
          activation,
          kernel_regularizer,
          bias_regularizer,
          use_bias,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/keras2/layers/core.py
imports: import sys
  import zoo.pipeline.api.keras.layers
  from zoo.pipeline.api.keras2.base import ZooKeras2Layer
label_desc: Use image node_id 1 to create a new Dense object from inherited base classes: ZooKeras2Layer with example: obj = Dense(units, kernel_initializer, bias_initializer, activation, kernel_regularizer, bias_regularizer, use_bias, input_dim, input_shape)