def sparse_or_dense_matvecmul(sparse_or_dense_matrix,<EOL>dense_vector,<EOL>validate_args=False,<EOL>name=None,<EOL>**kwargs):
with tf.compat.v1.name_scope(name, '<STR_LIT>',<EOL>[sparse_or_dense_matrix, dense_vector]):<EOL><INDENT>dense_vector = tf.convert_to_tensor(<EOL>value=dense_vector, dtype_hint=tf.float32, name='<STR_LIT>')<EOL>return tf.squeeze(<EOL>sparse_or_dense_matmul(<EOL>sparse_or_dense_matrix,<EOL>dense_vector[..., tf.newaxis],<EOL>validate_args=validate_args,<EOL>**kwargs),<EOL>axis=[-<NUM_LIT:1>])<EOL><DEDENT>
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`.