parameters: (a, dtype=None)
return_statement: return array(a, dtype, copy=False, order='C', ndmin=1)
docstring: Return a contiguous array (ndim >= 1) in memory (C order).
  
      Parameters
      ----------
      a : array_like
          Input array.
      dtype : str or dtype object, optional
          Data-type of returned array.
  
      Returns
      -------
      out : ndarray
          Contiguous array of same shape and content as `a`, with type `dtype`
          if specified.
  
      See Also
      --------
      asfortranarray : Convert input to an ndarray with column-major
                       memory order.
      require : Return an ndarray that satisfies requirements.
      ndarray.flags : Information about the memory layout of the array.
  
      Examples
      --------
      >>> x = np.arange(6).reshape(2,3)
      >>> np.ascontiguousarray(x, dtype=np.float32)
      array([[ 0.,  1.,  2.],
             [ 3.,  4.,  5.]], dtype=float32)
      >>> x.flags['C_CONTIGUOUS']
      True
  
      Note: This function returns an array with at least one-dimension (1-d) 
      so it will not preserve 0-d arrays.
docstring_summary: Return a contiguous array (ndim >= 1) in memory (C order).
docstring_tokens: Return a contiguous array ( ndim > = 1 ) in memory ( C order ) .
function: def ascontiguousarray(a, dtype=None):
      """
      Return a contiguous array (ndim >= 1) in memory (C order).
  
      Parameters
      ----------
      a : array_like
          Input array.
      dtype : str or dtype object, optional
          Data-type of returned array.
  
      Returns
      -------
      out : ndarray
          Contiguous array of same shape and content as `a`, with type `dtype`
          if specified.
  
      See Also
      --------
      asfortranarray : Convert input to an ndarray with column-major
                       memory order.
      require : Return an ndarray that satisfies requirements.
      ndarray.flags : Information about the memory layout of the array.
  
      Examples
      --------
      >>> x = np.arange(6).reshape(2,3)
      >>> np.ascontiguousarray(x, dtype=np.float32)
      array([[ 0.,  1.,  2.],
             [ 3.,  4.,  5.]], dtype=float32)
      >>> x.flags['C_CONTIGUOUS']
      True
  
      Note: This function returns an array with at least one-dimension (1-d) 
      so it will not preserve 0-d arrays.  
  
      """
      return array(a, dtype, copy=False, order='C', ndmin=1)
function_tokens: def ascontiguousarray ( a , dtype = None ) : return array ( a , dtype , copy = False , order = 'C' , ndmin = 1 )