if (!shared_name.empty()) {
      return shared_name;
    }
  }
  // Attempt to parse "_class" attribute if there is no "shared_name"
  // attribute.
  ArrayAttr classes_attr = op->getAttrOfType<ArrayAttr>(kClassAttr);
  if (!classes_attr) {
    // Attempt to parse "_class" from the IdentityOp that follows VariableV2.
    // For read-only reference variables, IdentityOp should be the only user of

  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/min"
  op: "Const"
  attr {

  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/min"
  op: "Const"
  attr {

  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/min"
  op: "Const"
  attr {

  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/min"
  op: "Const"
  attr {

  %1 = func.call @id(%arg0) : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>
  "tf.MakeIterator"(%1, %0) {_class = ["loc:@BatchDatasetV2"], device = ""} : (tensor<!tf_type.variant>, tensor<!tf_type.resource>) -> ()
  %2:2 = "tf.IteratorGetNext"(%0) {_class = ["loc:@iterator"], device = ""} : (tensor<!tf_type.resource>) -> (tensor<200x28x28x1xf32>, tensor<200x10xf32>)
  // CHECK: tf.Iterator

  NameAttrList name;
  name.set_name(def.op());
  *name.mutable_attr() = def.attr();
  // Remove the "_class" attribute from the attribute set used to create the
  // compilation cache key. This attribute is information for the colocator
  // and causes false uniqueness between nodes.
  name.mutable_attr()->erase("_class");
  return CompileImpl(compile_options, options, name, args, CompileScope::kOp,

  %48 = "tf.Identity"(%47) {device = ""} : (tensor<i1>) -> tensor<i1>
  %49 = "tf.Identity"(%36#1) {_class = ["loc:@WhitespaceTokenize/WhitespaceTokenizeWithOffsets"], device = ""} : (tensor<?xi64>) -> tensor<?xi64>

    The colocation class (`_class`) of the `Split` is set to the same class as
    its predecessor:

    ```mlir
    %outputs1:2, %control1 = tf_executor.island wraps "tf.IteratorGetNext"(%arg)
      {_class = ["loc:@dataset_iterator_1"]}
    %outputs2:2, %control2 = tf_executor.island wraps "tf.Split"(%outputs0, %outputs1#1)
      {_class = ["loc:@dataset_iterator_1", num_split = 2 : i32}
    ```

nsor<?xf32>>>, %arg23: tensor<!tf_type.resource<tensor<512x?xf32>>>, %arg24: tensor<!tf_type.resource<tensor<?x?xf32>>>, %arg25: tensor<!tf_type.resource<tensor<?xf32>>>, %arg26: tensor<!tf_type.resource<tensor<?x?xf32>>>, %arg27: tensor<f32> {tf._class = ["loc:@md/rnnt_step_decoder/rnnt_decoder/joint/w_b_y/var"]}, %arg28: tensor<!tf_type.resource<tensor<?xf32>>>, %arg29: tensor<!tf_type.resource<tensor<?xf32>>>, %arg30: tensor<!tf_type.resource<tensor<512xf32>>>, %arg31: tensor<!tf_type.resource<tensor<?xf32>>>,...