class_docstr: The layer that performs normalization using the formula:
  :math:`bn(x)[i][j] = ((x[i][j] - mean[j]) / \sqrt{var[j]}) * gamma[j] + beta[j]`
  
  - gamma and beta are the trainable parameters
  - mean and var depend on whether the layer is being trained:
  
      - If the layer is being trained, mean[j] and var[j] are 
        the mean value and the variance of x data with j 
        coordinate across all i.
      - If the layer is not being trained, mean[j] and var[j] are
        the exponential moving mean and the unbiased variance
        estimate calculated during training.
  
  :param input_layer: The input layer and the number of its output. If no number
      is specified, the first output will be connected.
  :type input_layer: object, tuple(object, int)
  :param channel_based: Turns on and off channel-based statistics:
  
      - If True, mean, var, gamma, and beta in the formula will be 
        vectors of the input **Channels** length. 
        The i coordinate will iterate over all values from 0 to 
        **BatchLength** * **BatchWidth** * **ListSize** * **Height** * **Width** * **Depth** - 1.
      - If False, the mean, var, gamma, and beta vectors 
        will have the **Height** * **Width** * **Depth** * **Channels** length. 
        The i coordinate will iterate over all values from 0 to 
        **BatchLength** * **BatchWidth** * **ListSize** - 1.
  :type channel_based: bool, default=True
  :param zero_free_term: Specifies if the free term (beta) should be trained or filled with zeros.
  :type zero_free_term: bool, default=False
  :param slow_convergence_rate: The coefficient for calculating the exponential moving mean and variance.
  :type slow_convergence_rate: float, default=1.0
  :param name: The layer name.
  :type name: str, default=None
docstr: Checks if the final parameters should be used for initialization.
          
docstr_tok: ["Checks","if","the","final","parameters","should","be","used","for","initialization","."]
code: def use_final_params_for_init(self):
      """Checks if the final parameters should be used for initialization."""
      return self._internal.get_use_final_params_for_init()
  
file_path: neoml/NeoML/Python/neoml/Dnn/BatchNormalization.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 10 for calling the BatchNormalization obj's underlying member method code with example usage: obj.use_final_params_for_init() and returns: self