class_docstr: The layer that calculates the cross-entropy loss function
  for binary classification:
  :math:`loss = - y * \log(sigmoid(x)) - (1 - y) * \log(1 - sigmoid(x))`, where
  x is the network response, y is the correct class label (can be -1 or 1)
  
  :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 positive_weight: The multiplier for correctly classified objects. 
      Tune this value to prioritize precision (positive_weight < 1) 
      or recall (positive_weight > 1) during training.
  :type positive_weight: float, default=1.0
  :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) the network response for which you are calculating the loss.
      It should contain the probability distribution for objects over classes.
  
  (2) the correct class labels (1 or -1). 
  
  (3) (optional): the objects' weights.
  
  The dimensions of all inputs are the same:
  
      - **BatchLength** * **BatchWidth** * **ListSize** - the number of objects
      - **Height**, **Width**, **Depth**, **Channels** should be equal to 1
  
  .. rubric:: Layer outputs:
  
  The layer has no output.
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docstr: Gets the multiplier for the term that corresponds 
  to the correct results.
docstr_tok: ["Gets","the","multiplier","for","the","term","that","corresponds","to","the","correct","results","."]
code: def positive_weight(self):
      """Gets the multiplier for the term that corresponds
      to the correct results.
      """
      return self._internal.get_positive_weight()
  
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
  from abc import ABCMeta, abstractmethod
  import neoml.Blob
label_desc: Use image node_id 2 for calling the BinaryCrossEntropyLoss obj's underlying member method code with example usage: obj.positive_weight() and returns: self