code: def sparse_or_dense_matvecmul(
      sparse_or_dense_matrix,
      dense_vector,
      validate_args=False,
      name=None,
      **kwargs,
  ):
      """Returns (batched) matmul of a (sparse) matrix with a column vector.
  
      Args:
        sparse_or_dense_matrix: `SparseTensor` or `Tensor` representing a (batch of)
          matrices.
        dense_vector: `Tensor` representing a (batch of) vectors, with the same
          batch shape as `sparse_or_dense_matrix`. The shape must be compatible with
          the shape of `sparse_or_dense_matrix` and kwargs.
        validate_args: When `True`, additional assertions might be embedded in the
          graph.
          Default value: `False` (i.e., no graph assertions are added).
        name: Python `str` prefixed to ops created by this function.
          Default value: 'sparse_or_dense_matvecmul'.
        **kwargs: Keyword arguments to `tf.sparse_tensor_dense_matmul` or
          `tf.matmul`.
  
      Returns:
        product: A dense (batch of) vector-shaped Tensor of the same batch shape and
        dtype as `sparse_or_dense_matrix` and `dense_vector`.
      """
      with tf.name_scope(name or "sparse_or_dense_matvecmul"):
          dense_vector = tf.convert_to_tensor(
              dense_vector, dtype_hint=tf.float32, name="dense_vector"
          )
          return tf.squeeze(
              sparse_or_dense_matmul(
                  sparse_or_dense_matrix,
                  dense_vector[..., tf.newaxis],
                  validate_args=validate_args,
                  **kwargs,
              ),
              axis=[-1],
          )
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 13 for calling a global function with example usage: sparse_or_dense_matvecmul(sparse_or_dense_matrix, dense_vector, validate_args, name) and returns: tf