class_docstr: A long short-term memory (LSTM) layer that can be applied to a set of 
  vector sequences. The output is a sequence containing the same number 
  of vectors, each of the layer hidden size length.
  
  :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) or list of them
  :param hidden_size: The size of hidden layer. 
      Affects the output size and the LSTM state vector size.
  :type hidden_size: int, default=1
  :param dropout_rate: The dropout probability for the input and the recurrent data.
  :type dropout_rate: float, default=0.0
  :param recurrent_activation: The activation function that is used in forget, reset, and input gates.
  :type recurrent_activation: str, {"linear", "elu", "relu", "leaky_relu", "abs", 
      "sigmoid", "tanh", "hard_tanh", "hard_sigmoid", 
      "power", "hswish", "gelu"}, default="sigmoid"
  :param reverse_seq: Indicates if the input sequence should be taken in the reverse order.
  :type reverse_seq: bool, default=False
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) the set of vector sequences, of dimensions:
  
      - **BatchLength** - the length of a sequence
      - **BatchWidth * ListSize** - the number of vector sequences in the input set
      - **Height * Width * Depth * Channels** - the size of each vector in the sequence
  
  (2) (optional): the initial state of the LSTM before the first step. 
      If this input is not specified, the initial state is all zeros.
      The dimensions:
  
      - **BatchLength** should be 1
      - the other dimensions should be the same as for the first input
  
  (3) (optional): the initial value of "previous output" to be used 
      on the first step. If this input is not specified, all zeros will be used.
      The dimensions:
  
      - **BatchLength** should be 1
      - the other dimensions should be the same as for the first input
  
  .. rubric:: Layer outputs:
  
  (1) the layer output on every step
  
  (2) (optional): the cell state on every step
  
  Both outputs have the following dimensions:
  
  - **BatchLength**, **BatchWidth**, **ListSize** are equal to the first input dimensions
  - **Height**, **Width**, **Depth** are equal to 1
  - **Channels** is equal to layer hidden size
docstr: Gets the dropout rate.
          
docstr_tok: ["Gets","the","dropout","rate","."]
code: def dropout(self):
      """Gets the dropout rate."""
      return self._internal.get_dropout()
  
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
  import neoml.Blob
label_desc: Use image node_id 6 for calling the Lstm obj's underlying member method code with example usage: obj.dropout() and returns: self