class_docstr: Graph Transformer.
  
  This is the bulk of the AutoDist backend logic, taking a single-node,
  single-GPU graph and transforming it into a distributed
  graph. This all happens based on the `Strategy` provided.
  
  The transformation occurs over several steps:
  
  1. Partitions the necessary variables
  2. Replicates the graph the desired number of times
  3. Within the graph, synchronizes gradients with in-graph logic
  4. Adds between-graph gradient synchronization logic
docstr: Call graph transformer to transform a graph item based on strategy and cluster.
docstr_tok: ["Call","graph","transformer","to","transform","a","graph","item","based","on","strategy","and","cluster","."]
code: def transform(self):
      """Call graph transformer to transform a graph item based on strategy and cluster."""
      logging.info(
          "Transforming the original graph to a distributed graph..."
      )
      with context.graph_mode():
          graph_item = self.graph_item
          # Ensure the transformation happens under graph mode, no matter the outer mode is under eager or graph.
  
          visualization_util.log_graph(
              graph=graph_item.graph, name="0-original"
          )
  
          (
              graph_item,
              self._strategy.node_config,
          ) = VariablePartitioner.apply(
              self._strategy.node_config, graph_item
          )
  
          visualization_util.log_graph(
              graph=graph_item.graph, name="1-after-partition"
          )
  
          # Create Synchronizers for each node in the strategy
          self._initialize_synchronizers()
  
          # Replicate the graph (both in-graph and between-graph)
          new_graph_item = Replicator.apply(
              config=self._strategy.graph_config.replicas,
              cluster=self._cluster,
              graph_item=graph_item,
          )
  
          # Apply synchronizers
          if self._num_local_replicas >= 1:
              new_graph_item = self._in_graph_apply(new_graph_item)
              logging.debug(
                  "Successfully applied local in-graph replication"
              )
              visualization_util.log_graph(
                  new_graph_item.graph, "2-after-in-graph"
              )
  
          if self._num_workers >= 1:
              new_graph_item = self._between_graph_apply(new_graph_item)
              logging.debug(
                  "Successfully applied between-graph replication"
              )
  
          final_item = new_graph_item
          logging.info("Successfully built the distributed graph.")
          visualization_util.log_graph(
              graph=final_item.graph, name="3-transformed"
          )
  
      return final_item
  
file_path: autodist/autodist/kernel/graph_transformer.py
imports: from tensorflow.core.framework.attr_value_pb2 import AttrValue
  from tensorflow.python.eager import context
  from tensorflow.python.framework import device_spec
  from autodist.graph_item import GraphItem
  from autodist.kernel.partitioner import VariablePartitioner
  from autodist.kernel.replicator import Replicator
  from autodist.kernel.synchronization.synchronizer import Synchronizer
  from autodist.utils import logging, visualization_util
label_desc: Use image node_id 2 for calling the GraphTransformer obj's underlying member method code with example usage: obj.transform() and returns: final_item