parameters: (math_engine, batch_len, batch_width, height, width, channels, dtype="float32")
return_statement: return Blob(PythonWrapper.tensor(math_engine._internal, shape, dtype))
docstring: Creates a blob with two-dimensional multi-channel images.
  
      :param neoml.MathEngine.MathEngine math_engine: the math engine that works with this blob.
  
      :param batch_len: the **BatchLength** dimension of the new blob.
      :type batch_len: int, > 0
  
      :param batch_width: the **BatchWidth** dimension of the new blob.
      :type batch_width: int, > 0
  
      :param height: the image height.
      :type height: int, > 0
  
      :param width: the image width.
      :type width: int, > 0
  
      :param channels: the number of channels in the image format.
      :type channels: int, > 0
  
      :param dtype: the type of data in the blob.
      :type dtype: str, {"float32", "int32"}, default="float32"
docstring_summary: Creates a blob with two-dimensional multi-channel images.
docstring_tokens: Creates a blob with two - dimensional multi - channel images .
function: def image2d(math_engine, batch_len, batch_width, height, width, channels, dtype="float32"):
      """Creates a blob with two-dimensional multi-channel images.
  
      :param neoml.MathEngine.MathEngine math_engine: the math engine that works with this blob.
  
      :param batch_len: the **BatchLength** dimension of the new blob.
      :type batch_len: int, > 0
  
      :param batch_width: the **BatchWidth** dimension of the new blob.
      :type batch_width: int, > 0
  
      :param height: the image height.
      :type height: int, > 0
  
      :param width: the image width.
      :type width: int, > 0
  
      :param channels: the number of channels in the image format.
      :type channels: int, > 0
  
      :param dtype: the type of data in the blob.
      :type dtype: str, {"float32", "int32"}, default="float32"
      """
      if dtype != "float32" and dtype != "int32":
          raise ValueError('The `dtype` must be one of {`float32`, `int32`}.')
      if batch_len < 1:
          raise ValueError('The `batch_len` must be > 0.')
      if batch_width < 1:
          raise ValueError('The `batch_width` must be > 0.')
      if height < 1:
          raise ValueError('The `height` must be > 0.')
      if width < 1:
          raise ValueError('The `width` must be > 0.')
      if channels < 1:
          raise ValueError('The `channels` must be > 0.')
  
      shape = np.array((batch_len, batch_width, 1, height, width, 1, channels), dtype=np.int32, copy=False)
  
      return Blob(PythonWrapper.tensor(math_engine._internal, shape, dtype))
function_tokens: def image2d ( math_engine , batch_len , batch_width , height , width , channels , dtype = "float32" ) : if dtype != "float32" and dtype != "int32" : raise ValueError ( 'The `dtype` must be one of {`float32`, `int32`}.' ) if batch_len < 1 : raise ValueError ( 'The `batch_len` must be > 0.' ) if batch_width < 1 : raise ValueError ( 'The `batch_width` must be > 0.' ) if height < 1 : raise ValueError ( 'The `height` must be > 0.' ) if width < 1 : raise ValueError ( 'The `width` must be > 0.' ) if channels < 1 : raise ValueError ( 'The `channels` must be > 0.' ) shape = np . array ( ( batch_len , batch_width , 1 , height , width , 1 , channels ) , dtype = np . int32 , copy = False ) return Blob ( PythonWrapper . tensor ( math_engine . _internal , shape , dtype ) )