class_docstr: The layer that repeats the input sequences several times.
  
  :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 repeat_count: The number of repetitions.
  :type repeat_count: int, > 0
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a sequence of objects.
  The dimensions:
  
      - **BatchLength** is the sequence length
      - **BatchWidth** * **ListSize** is the number of sequences in the set
      - **Height** * **Width** * **Depth** * **Channels** is the size of each object
  
  .. rubric:: Layer outputs:
  
  (1) the same sequence repeated repeat_count times.
  The dimensions:
  
      - **BatchLength** is repeat_count times larger than the input's **BatchLength**
      - all other dimensions are the same as for the input
code: def __init__(self, input_layer, repeat_count, name=None):
      if type(input_layer) is PythonWrapper.RepeatSequence:
          super().__init__(input_layer)
          return
  
      layers, outputs = check_input_layers(input_layer, 1)
  
      internal = PythonWrapper.RepeatSequence(
          str(name), layers[0], int(outputs[0]), int(repeat_count)
      )
      super().__init__(internal)
  
file_path: neoml/NeoML/Python/neoml/Dnn/RepeatSequence.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 1 to create a new RepeatSequence object from inherited base classes: Layer with example: obj = RepeatSequence(input_layer, repeat_count, name)