code: def make_nd_vec(v, nd=None, t=None, norm=False):
      
  
      # Imports
      import numpy as np
      from scipy import linalg as spla
  
      # Reduce the input to the extent possible
      out_v = np.array(v, dtype=t).squeeze()
  
      # Confirm vector form
      if not len(out_v.shape) == 1:
          raise ValueError("'v' is not reducible to a vector.")
      ## end if
  
      # If indicated, confirm dimensionality
      if nd and not out_v.shape[0] == nd:
          raise ValueError("'v' dimension is " + str(out_v.shape[0]) +
                  ", not " + str(nd))
      ## end if
  
      # Normalize, if indicated
      if norm:
          out_v = out_v / spla.norm(out_v)
      ## end if
  
      # Return result
      return out_v
docs: Coerce input to np.array() and validate dimensionality.
  
      Ensure dimensionality 'n' if passed.
      Cast to type 't' if passed
      Normalize output if norm=True
  
      .. todo:: Complete make_nd_vec docstring