class_docstr: The layer that creates a set of two-dimensional images using a set of
  pixel sequences with specified coordinates.
  
  :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: list of object, tuple(object, int)
  :param height: The height of the resulting images.  
  :type height: int
  :param width: The width of the resulting images.    
  :type width: int
  :param name: The layer name.    
  :type name: str, default=None
      
  
  .. rubric:: Layer inputs:
  
  (1) a blob with pixel sequences.
      The dimensions:
  
      - **BatchLength** is 1
      - **BatchWidth** is the number of sequences in the set
      - **ListSize** is the length of each sequence
      - **Height**, **Width**, **Depth** are 1
      - **Channels** is the number of channels for the pixel sequences 
        and the output images.
  
  (2) a blob with integer data that contains lists of pixel coordinates.
      The dimensions:
  
      - **BatchWidth**, **ListSize** are the same as for the first input
      - the other dimensions are 1
  
  .. rubric:: Layer outputs:
  
  (1) a blob with images.
      The dimensions:
  
      - **BatchLength** is 1
      - **BatchWidth** is the same as for the first input
      - **ListSize** is 1
      - **Height** is the specified image height
      - **Width** is the specified image width
      - **Depth** is 1
      - **Channels** is the same as for the first input
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docstr: Gets the output image width.
          
docstr_tok: ["Gets","the","output","image","width","."]
code: def width(self):
      """Gets the output image width."""
      return self._internal.get_width()
  
file_path: neoml/NeoML/Python/neoml/Dnn/ImageConversion.py
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 PixelToImage obj's underlying member method code with example usage: obj.width() and returns: self