class_docstr: Echo State Network layer.
  
  This implements the recurrent layer using the ESNCell.
  
  This is based on the paper
      H. Jaeger
      ["The "echo state" approach to analysing and training recurrent neural networks"]
      (https://www.researchgate.net/publication/215385037).
      GMD Report148, German National Research Center for Information Technology, 2001.
  
  Args:
      units: Positive integer, dimensionality of the reservoir.
      connectivity: Float between 0 and 1.
          Connection probability between two reservoir units.
          Default: 0.1.
      leaky: Float between 0 and 1.
          Leaking rate of the reservoir.
          If you pass 1, it's the special case the model does not have leaky integration.
          Default: 1.
      spectral_radius: Float between 0 and 1.
          Desired spectral radius of recurrent weight matrix.
          Default: 0.9.
      use_norm2: Boolean, whether to use the p-norm function (with p=2) as an upper
          bound of the spectral radius so that the echo state property is satisfied.
          It  avoids to compute the eigenvalues which has an exponential complexity.
          Default: False.
      use_bias: Boolean, whether the layer uses a bias vector.
          Default: True.
      activation: Activation function to use.
          Default: hyperbolic tangent (`tanh`).
          If you pass `None`, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      kernel_initializer: Initializer for the `kernel` weights matrix,
          used for the linear transformation of the inputs.
          Default: `glorot_uniform`.
      recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix,
          used for the linear transformation of the recurrent state.
          Default: `glorot_uniform`.
      bias_initializer: Initializer for the bias vector.
          Default: `zeros`.
      return_sequences: Boolean. Whether to return the last output.
          in the output sequence, or the full sequence.
      go_backwards: Boolean (default False).
          If True, process the input sequence backwards and return the
          reversed sequence.
      unroll: Boolean (default False).
          If True, the network will be unrolled,
          else a symbolic loop will be used.
          Unrolling can speed-up a RNN,
          although it tends to be more memory-intensive.
          Unrolling is only suitable for short sequences.
  
  Call arguments:
      inputs: A 3D tensor.
      mask: Binary tensor of shape `(samples, timesteps)` indicating whether
          a given timestep should be masked.
      training: Python boolean indicating whether the layer should behave in
          training mode or in inference mode. This argument is passed to the cell
          when calling it. This is only relevant if `dropout` or
          `recurrent_dropout` is used.
      initial_state: List of initial state tensors to be passed to the first
          call of the cell.
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code: def use_bias(self):
      return self.cell.use_bias
  
file_path: addons/tensorflow_addons/layers/esn.py
imports: import tensorflow
  from tensorflow_addons.rnn import ESNCell
  from typeguard import typechecked
  from tensorflow_addons.utils.types import Activation, FloatTensorLike, TensorLike, Initializer
label_desc: Use image node_id 8 for calling the ESN obj's underlying member method code with example usage: obj.use_bias() and returns: self