code: def expand_to_vector(
      x, tensor_name=None, op_name=None, validate_args=False
  ):
      """Transform a 0-D or 1-D `Tensor` to be 1-D.
  
      For user convenience, many parts of the TensorFlow Probability API accept
      inputs of rank 0 or 1 -- i.e., allowing an `event_shape` of `[5]` to be passed
      to the API as either `5` or `[5]`.  This function can be used to transform
      such an argument to always be 1-D.
  
      NOTE: Python or NumPy values will be converted to `Tensor`s with standard type
      inference/conversion.  In particular, an empty list or tuple will become an
      empty `Tensor` with dtype `float32`.  Callers should convert values to
      `Tensor`s before calling this function if different behavior is desired
      (e.g. converting empty lists / other values to `Tensor`s with dtype `int32`).
  
      Args:
        x: A 0-D or 1-D `Tensor`.
        tensor_name: Python `str` name for `Tensor`s created by this function.
        op_name: Python `str` name for `Op`s created by this function.
        validate_args: Python `bool, default `False`.  When `True`, arguments may be
          checked for validity at execution time, possibly degrading runtime
          performance.  When `False`, invalid inputs may silently render incorrect
            outputs.
      Returns:
        vector: a 1-D `Tensor`.
      """
      with tf.name_scope(op_name or "expand_to_vector"):
          x_orig = x
          x = ps.convert_to_shape_tensor(x, name="x")
          ndims = tensorshape_util.rank(x.shape)
  
          if ndims is None:
              # Maybe expand ndims from 0 to 1.
              if validate_args:
                  x = with_dependencies(
                      [
                          assert_util.assert_rank_at_most(
                              x,
                              1,
                              message="Input is neither scalar nor vector.",
                          )
                      ],
                      x,
                  )
              ndims = ps.rank(x)
              expanded_shape = pick_vector(
                  ps.equal(ndims, 0),
                  np.array([1], dtype=np.int32),
                  ps.shape(x),
              )
              return ps.reshape(x, expanded_shape)
  
          elif ndims == 0:
              # Definitely expand ndims from 0 to 1.
              return ps.convert_to_shape_tensor(
                  ps.reshape(x_orig, [1]), name=tensor_name
              )
  
          elif ndims != 1:
              raise ValueError("Input is neither scalar nor vector.")
  
          # ndims == 1
          return x
  
imports: import functools
  import hashlib
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 40 for calling a global function with example usage: expand_to_vector(x, tensor_name, op_name, validate_args) and returns: x, ps, ps