class_docstr: The fully connected layer.
  It multiplies each of the input vectors by the weight matrix
  and adds the free term vector to the result.
  
  :param input_layers: The input layers to be connected. 
      The integer in each tuple specifies the number of the output.
      If not set, the first output will be used.
  :type input_layers: list of object, tuple(object, int)
  :param element_count: The length of each vector in the output.
  :type element_count: int, > 0
  :param  is_zero_free_term: If True, the free term vector is set to all zeros and not trained.
      If False, the free term is trained together with the weights.
  :type is_zero_free_term: bool, default=False  
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  The layer can have any number of inputs.
  The dimensions:
  
  - **BatchLength** * **BatchWidth** * **ListSize** is the number of vectors
  - **Height** * **Width** * **Depth** * **Channels** is the vector size; 
    should be the same for all inputs
  
  .. rubric:: Layer outputs:
  
  The layer returns one output for each input.
  The dimensions:
  
  - **BatchLength**, **BatchWidth**, **ListSize** the same as for the input
  - **Height**, **Width**, **Depth** are 1
  - **Channels** is element_count
docstr: Applies batch normalization to this layer.
  Batch normalization must be deleted from the dnn afterwards
  and layers which were connected to the batch norm must be connected to this layer.
  
  :param neoml.Dnn.BatchNormalization layer: batch norm to be applied
docstr_tok: ["Applies","batch","normalization","to","this","layer",".","Batch","normalization","must","be","deleted","from","the","dnn","afterwards","and","layers","which","were","connected","to","the","batch","norm","must","be","connected","to","this","layer",".",":","param","neoml.Dnn.BatchNormalization","layer",":","batch","norm","to","be","applied"]
code: def apply_batch_normalization(self, layer):
      """Applies batch normalization to this layer.
      Batch normalization must be deleted from the dnn afterwards
      and layers which were connected to the batch norm must be connected to this layer.
  
      :param neoml.Dnn.BatchNormalization layer: batch norm to be applied
      """
      if type(layer) is not BatchNormalization:
          raise ValueError(
              "The `layer` must be neoml.Dnn.BatchNormalization."
          )
  
      self._internal.apply_batch_normalization(layer._internal)
  
file_path: neoml/NeoML/Python/neoml/Dnn/FullyConnected.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
  from .BatchNormalization import BatchNormalization
  import neoml.Blob
label_desc: Use image node_id 5 for calling the FullyConnected obj's underlying member method code with example usage: obj.apply_batch_normalization(layer) without return types