code: def _ln(input_tensor, gain, bias, epsilon=1e-5, axes=None):
      """
      Apply layer normalisation.
  
      :param input_tensor: (TensorFlow Tensor) The input tensor for the Layer normalization
      :param gain: (TensorFlow Tensor) The scale tensor for the Layer normalization
      :param bias: (TensorFlow Tensor) The bias tensor for the Layer normalization
      :param epsilon: (float) The epsilon value for floating point calculations
      :param axes: (tuple, list or int) The axes to apply the mean and variance calculation
      :return: (TensorFlow Tensor) a normalizing layer
      """
      if axes is None:
          axes = [1]
      mean, variance = tf.nn.moments(
          input_tensor, axes=axes, keep_dims=True
      )
      input_tensor = (input_tensor - mean) / tf.sqrt(variance + epsilon)
      input_tensor = input_tensor * gain + bias
      return input_tensor
  
file_path: stable-baselines/stable_baselines/common/tf_layers.py
imports: import numpy
  import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: _ln(input_tensor, gain, bias, epsilon, axes) and returns: input_tensor