class_docstr: Just your regular densely-connected NN layer.
  `Dense` implements the operation:
  `output = activation(dot(input, kernel) + bias)`
  where `activation` is the element-wise activation function
  passed as the `activation` argument, `kernel` is a weights matrix
  created by the layer, and `bias` is a bias vector created by the layer
  (only applicable if `use_bias` is `True`).
  
  Arguments:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use.
          If you don't specify anything, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix.
      bias_initializer: Initializer for the bias vector.
      kernel_regularizer: Regularizer function applied to
          the `kernel` weights matrix.
      bias_regularizer: Regularizer function applied to the bias vector.
      activity_regularizer: Regularizer function applied to
          the output of the layer (its "activation").
      kernel_constraint: Constraint function applied to
          the `kernel` weights matrix.
      bias_constraint: Constraint function applied to the bias vector.
  
  Input shape:
      2D tensor with shape: `(batch_size, input_dim)`.
  
  Output shape:
      2D tensor with shape: `(batch_size, units)`.
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code: def __init__(
      self,
      units,
      activation=None,
      use_bias=True,
      kernel_initializer="glorot_uniform",
      bias_initializer="zeros",
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      kernel_constraint=None,
      bias_constraint=None,
      **kwargs,
  ):
      super(Dense, self).__init__(**kwargs)
  
      self.units = int(units)
      self.activation_identifier = activation
      self.activation = activations.get(self.activation_identifier)
      self.use_bias = use_bias
  
      self.kernel_initializer = initializers.get(kernel_initializer)
      self.bias_initializer = initializers.get(bias_initializer)
  
      # Not implemented arguments
      default_args_check(
          kernel_regularizer, "kernel_regularizer", "Dense"
      )
      default_args_check(bias_regularizer, "bias_regularizer", "Dense")
      default_args_check(
          activity_regularizer, "activity_regularizer", "Dense"
      )
      default_args_check(
          kernel_constraint, "kernel_constraint", "Dense"
      )
      default_args_check(bias_constraint, "bias_constraint", "Dense")
  
file_path: tf-encrypted/tf_encrypted/keras/layers/dense.py
imports: from tensorflow.python.keras import initializers
  import tf_encrypted
  from tf_encrypted.keras import activations
  from tf_encrypted.keras.engine import Layer
  from tf_encrypted.keras.layers.layers_utils import default_args_check
label_desc: Use image node_id 1 to create a new Dense object from inherited base classes: Layer with example: obj = Dense(units, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint)