class_docstr: Linear normalization layer which scales and shifts the input to [-2.0, 2.0], for bounded states
  with min/max_value (specification key: `linear_normalization`).
  
  Args:
      min_value (float | array[float]): Lower bound of the value
          (<span style="color:#00C000"><b>default</b></span>: based on input_spec).
      max_value (float | array[float]): Upper bound of the value range
          (<span style="color:#00C000"><b>default</b></span>: based on input_spec).
      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>.
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code: def default_input_spec(self):
      return TensorSpec(
          type="float",
          shape=None,
          min_value=self.min_value,
          max_value=self.max_value,
      )
  
imports: import numpy
  import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import parameter_modules, TensorSpec, tf_function, tf_util
  from tensorforce.core.layers import Layer, StatefulLayer
label_desc: Use image node_id 2 for calling the LinearNormalization obj's underlying member method code with example usage: obj.default_input_spec() and returns: TensorSpec