class ConvertEmbeddedLookupFunc {
 public:
  explicit ConvertEmbeddedLookupFunc(func::FuncOp func) : func_(func) {}

  void RewriteFunc() {
    func_->setAttr(kTFImplements,
                   StringAttr::get(func_.getContext(), "embedding_lookup"));
    Value lookup = func_.getArgument(1);
    Value value = func_.getArgument(0);

    if (!preceding_qcast) return failure();

    auto new_qcast = rewriter.create<quantfork::QuantizeCastOp>(
        q_op.getLoc(), q_op.getType(), preceding_qcast.getArg());
    new_qcast->setAttr(kVolatileOpAttrName, rewriter.getUnitAttr());
    q_op->replaceAllUsesWith(new_qcast);
    return success();
  }
};

bool PrepareQuantizePass::ContainsQuantizeOps(func::FuncOp func) {

        // tensorflow/lite/kernels/fully_connected.cc. The kernels will handle
        // the asymmetric_quantize_inputs attribute in the builtin option.
        dynamic_range_quantized_user->setAttr(
            kAsymmetricQuantizeInputsAttr,
            BoolAttr::get(rewriter.getContext(), true));
        changed = true;
      }
    }
    return changed;
  }

  xla::OpSharding sharding =
      manual_sharding ? ::xla::sharding_builder::Manual()
                      : ::xla::sharding_builder::AssignDevice(kShardingTpuCore);
  op->setAttr(kShardingAttr,
              StringAttr::get(op->getContext(), sharding.SerializeAsString()));
}

// Assigns frontend attributes holding information about data type and

        to_keep.push_back(sym_ref);
      }
    }

    for (auto func_op : to_remove) rewriter.eraseOp(func_op);

    if (to_keep.empty())
      rewriter.eraseOp(op);
    else
      op->setAttr("initializers", rewriter.getArrayAttr(to_keep));

    return success();
  }
};

void SessionInitializerOp::getCanonicalizationPatterns(
    RewritePatternSet &results, MLIRContext *context) {

    for (NamedAttribute attr : op->getAttrs()) {
      auto symref = mlir::dyn_cast<FlatSymbolRefAttr>(attr.getValue());
      if (!symref) continue;
      if (symref.getValue() != func.getName()) continue;
      op->setAttr(attr.getName(),
                  FlatSymbolRefAttr::get(op->getContext(), cloned.getName()));
      break;
    }
  }

  // Traverse results backward so that indices to be deleted stay unchanged.

              .getInt();
      arg_metadata->set_retval_index_for_sharding(ret_index);
    }
  }
  // Update the metadata of the compile op.
  compile.setAttr("metadata", StringAttr::get(compile.getContext(),
                                              metadata.SerializeAsString()));
  return mapping;
}

// Adds a new replicated input to the replicate op.

      updated_input_shapes_attr.emplace_back(input_shapes_attr[i]);
    }

    // Replaces the attribute with the updated "#tf_type.shape" array.
    // Builder builder(func_op.getContext());
    func_op->setAttr(
        kTfInputShapesAttr,
        ArrayAttr::get(func_op.getContext(), updated_input_shapes_attr));
  }
}

// Validates func ops. Returns `failure` if the function is invalid.

                                            key_and_value_pair[0],
                                            "' attribute"));
        continue;
      }
      inner_op.setAttr(llvm::StringRef(std::string(key_and_value_pair[0])),
                       identifier_to_attr[llvm::StringRef(
                           std::string(key_and_value_pair[1]))]);
    }

      // Add or update the derived attribute with the value. Skip the fixed
      // element type attributes, in case they are present in the NodeDef.
      if (!fixed_elt_type_attrs_.contains(attr.first())) {
        new_op->setAttr(attr.first(), attr.second);
      }
    }
  }
  // Create the tfr.cast ops on the results and replace the uses of the
  // original call op.
  TFRTensorType unconstrainted_type = rewriter.getType<TFRTensorType>();