code: def multi_dot(arrays, *, precision=None):
      check_arraylike("jnp.linalg.multi_dot", *arrays)
      n = len(arrays)
      # optimization only makes sense for len(arrays) > 2
      if n < 2:
          raise ValueError("Expecting at least two arrays.")
      elif n == 2:
          return jnp.dot(arrays[0], arrays[1], precision=precision)
  
      arrays = [jnp.asarray(a) for a in arrays]
  
      # save original ndim to reshape the result array into the proper form later
      ndim_first, ndim_last = arrays[0].ndim, arrays[-1].ndim
      # Explicitly convert vectors to 2D arrays to keep the logic of the internal
      # _multi_dot_* functions as simple as possible.
      if arrays[0].ndim == 1:
          arrays[0] = jnp.atleast_2d(arrays[0])
      if arrays[-1].ndim == 1:
          arrays[-1] = jnp.atleast_2d(arrays[-1]).T
      _assert2d(*arrays)
  
      # _multi_dot_three is much faster than _multi_dot_matrix_chain_order
      if n == 3:
          result = _multi_dot_three(*arrays, precision)
      else:
          order = _multi_dot_matrix_chain_order(arrays)
          result = _multi_dot(arrays, order, 0, n - 1, precision)
  
      # return proper shape
      if ndim_first == 1 and ndim_last == 1:
          return result[0, 0]  # scalar
      elif ndim_first == 1 or ndim_last == 1:
          return result.ravel()  # 1-D
      else:
          return result
  
file_path: jax/jax/_src/third_party/numpy/linalg.py
imports: import numpy
  import jax.numpy
  import jax.numpy.linalg
  from jax._src.numpy.util import check_arraylike, _wraps
label_desc: Use image node_id 9 for calling a global function with example usage: multi_dot(arrays) and returns: result, jnp, result, result