class_docstr: Linear layer (specification key: `linear`).
  
  Args:
      size (int >= 0): Layer output size, 0 implies additionally removing the axis
          (<span style="color:#C00000"><b>required</b></span>).
      bias (bool): Whether to add a trainable bias variable
          (<span style="color:#00C000"><b>default</b></span>: true).
      initialization_scale (float > 0.0): Initialization scale
          (<span style="color:#00C000"><b>default</b></span>: 1.0).
      vars_trainable (bool): Whether layer variables are trainable
          (<span style="color:#00C000"><b>default</b></span>: true).
      l2_regularization (float >= 0.0): Scalar controlling L2 regularization
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      name (string): Layer name
          (<span style="color:#00C000"><b>default</b></span>: internally chosen).
      input_spec (specification): <span style="color:#00C000"><b>internal use</b></span>.
code: def apply(self, *, x):
      if len(self.input_spec.shape) == 0:
          x = tf.expand_dims(input=x, axis=1)
  
      x = self.linear.apply(x=x)
  
      return x
  
file_path: tensorforce/tensorforce/core/layers/linear.py
imports: import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import tf_function, TensorSpec
  from tensorforce.core.layers import Conv1d, Conv2d, Dense, Layer
label_desc: Use image node_id 4 for calling the Linear obj's underlying member method code with example usage: obj.apply() and returns: x