<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def fix_colocation_after_import(input_map, absolute_import_scope):
 """Fixes colocation attributes after import according to input_map. This function is meant to be called after importing a GraphDef, in order to rewrite colocate_with constrains analogous to how inputs to ops are rewritten by input_map during import. It also updates devices accordingly. The nodes in the given import scope of the current default graph have their rewritten as follows: If, before the call, op x has attribute loc:@y, and `input_map` replaces an output of y with an output of z, then loc:@y gets replaced by the colocation attributes of z (that is, loc:@z, if no other constraints are in play). This style of rewriting imposes the following requirements: * If an output of node y is an input tensor in a signature of the module, y must not have any colocation attributes on it, such that colocations with y are expressed by loc:@y and can be adjusted with a rewriting rule for it. Function `find_signature_input_colocation_error()` checks this during module creation. * If y1 is a state node, its colocation constraints must only reference other state nodes, say, y2. Since all outputs of state nodes are mapped the same way, all their rewriting rules together will do the same thing. Function `find_state_op_colocation_error()` checks this during module creation. * Other nodes may have arbitrary colocation attributes. Mapping of inputs works with tensors, while colocation constraints work with ops. Issues may arise when mapping tensors from ops with multiple outputs. colocation_groups) If some but not all outputs of y are replaced, it becomes ambiguous whether to rewrite loc:@y at all. For now, this is handled conservatively by raising an error (instead of rewriting to the union of all applicable constraints). This should be very rare: all state ops so far have single outputs (and even if not, the rewriting would be consistent); input ops usually are placeholders, which have single outputs. Args: input_map: a dict mapping from tensor names in the imported graph to existing Tensors, typically the same as passed to tf.import_graph_def(). absolute_import_scope: a string with the full name of the import scope, comprising the current scope when import_graph_def() as called plus the import_scope passed to it. Raises: ValueError: if one imported op has its multiple outputs and they are remapped in a way that causes conflicting colocation rewrites. """

  attr_map = _build_colocation_attr_map(input_map, absolute_import_scope)
  _apply_colocation_attr_map(attr_map, absolute_import_scope)