// limitations under the License.

// Revision History
// Version 0: Initial version.
// Version 1: Add subgraphs to schema.
// Version 2: Rename operators to conform to NN API.
// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
// Version 3a: Add new builtin op code field. Has backward compatibility with
//             version 3.

  // operation dominates all the users (%3 in this example).
  Operation *insertion_point;

  // After construction:
  //   A set of constraints on the clustered operation operands that must be
  //   satisfied in order to add operation to the cluster. For basic block
  //   source this will be always empty.
  //
  // During the union-find cluster formation:
  //   The root member will have constraints merged from all of the cluster

    devices.insert({device_alias, device_list_for_alias});
  }

  OpBuilder builder(replicate);
  builder.setInsertionPoint(while_op);
  // Create per-device variables for formatting state, and add them to the while
  // loop.
  auto key_type =
      RankedTensorType::get({2}, TF::StringType::get(builder.getContext()));
  auto state_vars =
      CreateStateVars(devices, while_op.getLoc(), key_type, &builder);

        %add = "tf.AddV2"(%cst, %cst) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
        %var_handle = "tf.VarHandleOp"() {container = "", device = "/device:CPU:0", shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
        "tf.AssignVariableOp"(%var_handle, %add) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
        return
      }

// Check that there are no extra "tf.Const"s existing in this function.
// CHECK-NOT: "tf.Const"

// Check that the usages of %[[CST]] and %[[CST_0]] are untouched.
// CHECK: %[[ADD:.*]] = "tf.AddV2"(%[[CST]], %[[CST_0]])
// CHECK: "tf.Max"({{.*}}, %[[ADD]])

// -----

// CHECK-LABEL: @recursively_duplicate_constants
func.func private @recursively_duplicate_constants(%arg0: tensor<1x2x3xf32>) -> tensor<1x3xf32> {

      edges.push_back(EdgeInfo{e->dst_input(), e->dst()->id()});
      VLOG(4) << "Oc -> oc edge: " << e->DebugString();
    }
  }

  // Remove the edge from host to outside compilation. Add a placeholder as
  // outside compilation node input.
  std::map<std::pair<string, int>, Node*> placeholders;
  for (int i = 0, end = edges.size(); i < end; i++) {
    Node* dst = g->FindNodeId(edges[i].dst_node_id);

[Device plugins](https://www.tensorflow.org/install/gpu_plugins#available_devices).

A smaller CPU-only package is also available:

```
$ pip install tensorflow-cpu
```

To update TensorFlow to the latest version, add `--upgrade` flag to the above
commands.

*Nightly binaries are available for testing using the
[tf-nightly](https://pypi.python.org/pypi/tf-nightly) and

  auto compilation_status_type =
      RankedTensorType::get({}, builder->getType<TF::StringType>());
  auto program_type =
      RankedTensorType::get({3}, builder->getType<TF::StringType>());

  // Add MLIR module's fingerprint to compile metadata.
  uint64_t mlir_fingerprint = tensorflow::Fingerprint64(txt_module);
  metadata.set_mlir_fingerprint(mlir_fingerprint);

  std::string txt_metadata;

      tensorflow::DeviceNameUtils::ParsedName name;
      if (tensorflow::DeviceNameUtils::ParseFullName(device.str(), &name)) {
        if (name.type == "TPU_REPLICATED_CORE") {
          // TODO(hanxiongwang): Add check for out of bound of name.id
          return llvm::StringRef(logical_device_vec[name.id]);
        }
      }
    }
  }
  return std::nullopt;
}

      } else if (quant_specs_.inference_type == tensorflow::DT_HALF) {
        quantizeOpAsFloat16(rewriter, op, quant_op);
        quantized = true;
      }
    }
    return quantized;
  }

  // Add asymmetric input quantization attribute. MLIR dynamic quantization
  // supports only the case that the value of the attribute equals to true. For
  // details, see tensorflow/compiler/mlir/lite/quantization/quantization.td