class_docstr: The layer that calculates the loss function used for training a CRF.
  The value is -log(probability of the correct class sequence)
  
  :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: array of (object, int) tuples or objects
  :param loss_weight: The multiplier for the loss function value during training.
  :type loss_weight:  float, default=1.0
  :param name: The layer name.
  :type name: str, default=None
      
  .. rubric:: Layer inputs:
  
  (1) a blob with integer data that contains optimal class sequences.
      The dimensions:
      - **BatchLength**, **BatchWidth** equal to the network inputs'
      - **Channels** equal to the number of classes
      - the other dimensions are equal to 1
  
  (2) a blob with float data containing non-normalized logarithm 
      of probabilities of the optimal class sequences.
      The dimensions are the same as for the first input.
  
  (3) a blob with float data containing non-normalized logarithm
      of probability of the correct class being in this position.
      The dimensions:
  
      - **BatchLength**, **BatchWidth** the same as for the first input
      - the other dimensions equal to 1
  
  .. rubric:: Layer outputs:
  
  The layer has no output.
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docstr: Sets the multiplier for the loss function value during training.
          
docstr_tok: ["Sets","the","multiplier","for","the","loss","function","value","during","training","."]
code: def loss_weight(self, loss_weight):
      """Sets the multiplier for the loss function value during training."""
      self._internal.set_loss_weight(float(loss_weight))
  
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 4 for calling the CrfLoss obj's underlying member method code with example usage: obj.loss_weight(loss_weight) without return types