parameters: (shape, dtype=None, order='C', device=None)
return_statement: return _mx_nd_np.zeros(shape, dtype, order, device)
docstring: Return a new array of given shape and type, filled with zeros.
      This function currently only supports storing multi-dimensional data
      in row-major (C-style).
  
      Parameters
      ----------
      shape : int or tuple of int
          The shape of the empty array.
      dtype : str or numpy.dtype, optional
          An optional value type,
          When npx.is_np_default_dtype() returns False, default dtype is float32,
          When npx.is_np_default_dtype() returns True, default dtype is float64.
          Note that this behavior is different from NumPy's `zeros` function where `float64`
          is the default value, here we can set 'float32' or 'float64' as your default dtype,
          because `float32` is considered as the default data type in deep learning.
      order : {'C'}, optional, default: 'C'
          How to store multi-dimensional data in memory, currently only row-major
          (C-style) is supported.
      device : Device, optional
          Device context on which the memory is allocated. Default is
          `mxnet.device.current_device()`.
  
      Returns
      -------
      out : ndarray
          Array of zeros with the given shape, dtype, and device.
  
      Examples
      --------
      >>> np.zeros(5)
      array([0., 0., 0., 0., 0.])
  
      >>> np.zeros((5,), dtype=int)
      array([0, 0, 0, 0, 0], dtype=int64)
  
      >>> np.zeros((2, 1))
      array([[0.],
             [0.]])
docstring_summary: Return a new array of given shape and type, filled with zeros.
      This function currently only supports storing multi-dimensional data
      in row-major (C-style).
docstring_tokens: Return a new array of given shape and type filled with zeros . This function currently only supports storing multi - dimensional data in row - major ( C - style ) .
function: def zeros(shape, dtype=None, order='C', device=None):  # pylint: disable=redefined-outer-name
      """Return a new array of given shape and type, filled with zeros.
      This function currently only supports storing multi-dimensional data
      in row-major (C-style).
  
      Parameters
      ----------
      shape : int or tuple of int
          The shape of the empty array.
      dtype : str or numpy.dtype, optional
          An optional value type,
          When npx.is_np_default_dtype() returns False, default dtype is float32,
          When npx.is_np_default_dtype() returns True, default dtype is float64.
          Note that this behavior is different from NumPy's `zeros` function where `float64`
          is the default value, here we can set 'float32' or 'float64' as your default dtype,
          because `float32` is considered as the default data type in deep learning.
      order : {'C'}, optional, default: 'C'
          How to store multi-dimensional data in memory, currently only row-major
          (C-style) is supported.
      device : Device, optional
          Device context on which the memory is allocated. Default is
          `mxnet.device.current_device()`.
  
      Returns
      -------
      out : ndarray
          Array of zeros with the given shape, dtype, and device.
  
      Examples
      --------
      >>> np.zeros(5)
      array([0., 0., 0., 0., 0.])
  
      >>> np.zeros((5,), dtype=int)
      array([0, 0, 0, 0, 0], dtype=int64)
  
      >>> np.zeros((2, 1))
      array([[0.],
             [0.]])
      """
      return _mx_nd_np.zeros(shape, dtype, order, device)
function_tokens: def zeros ( shape , dtype = None , order = 'C' , device = None ) : # pylint: disable=redefined-outer-name return _mx_nd_np . zeros ( shape , dtype , order , device )