def FuseAffineOpAndMul : Pat<
  (TF_MulOp
    (SupportedAffineOpMatcher $conv_out, $input, $weight),
    (TF_ConstOp:$mul_rhs IsFloatElementsAttr:$mul_rhs_value)),
  (CloneOpWithReplacedOperands
        (GetDefiningOp $conv_out),
        $input,
        (MultiplyFakeQuantValue $weight,
          (MakeOneDimValueBroadcastable $mul_rhs, $weight))),
  [(HasOneUse $conv_out),
   (HasRankOf<1> $mul_rhs_value),

    auto var_handle_op =
        dyn_cast<TF::VarHandleOp>(resource_operand.getDefiningOp());
    if (!var_handle_op) continue;

    Value assigned_value_operand = assign_variable_op.getOperand(1);
    auto const_op =
        dyn_cast<TF::ConstOp>(assigned_value_operand.getDefiningOp());
    if (!const_op) continue;

    var_handle_ops.emplace_back(var_handle_op);
  }

  quant_spec.set_tensor_type(QuantizationComponentSpec::TENSORTYPE_INT_8);

  std::optional<TF::PartitionedCallOp> dequantize_op = ApplyUniformQuantization(
      pattern_rewriter, cast<TF::ConstOp>(value.getDefiningOp()), quant_spec);
  EXPECT_TRUE(dequantize_op.has_value());
  EXPECT_EQ(dequantize_op.value().func().getName().str(),
            "composite_dequantize_uniform");
}

}  // namespace

    getUsedValuesDefinedAbove(op.getRegions(), live_ins);
    // Inserts if any of the `live_ins` depends on the ops in the cluster.
    if (llvm::any_of(live_ins, [&](Value value) {
          Operation* defining_op = value.getDefiningOp();
          if (!defining_op) {
            return false;
          }
          return c.ops.contains(defining_op) ||
                 ops_depend_on_cluster.contains(defining_op);
        })) {

  // This value isn't an expressed type (float), skip.
  if (!new_type) return;

  Block &block = value.getParentRegion()->front();
  Operation *op = value.getDefiningOp();
  if (op) {
    builder.setInsertionPoint(&block, ++Block::iterator(op));
  } else {
    builder.setInsertionPointToStart(&block);
  }
  TypeAttr type_attr = TypeAttr::get(new_type);

    }
  }

  erase_list.push_back(op);

  for (auto& use : op->getOpOperands()) {
    if (auto op_result = mlir::dyn_cast<mlir::OpResult>(use.get())) {
      Operation* def = op_result.getDefiningOp();
      if (!dead_ops.insert(def).second) continue;
      RecursiveRemove(def, erase_list, dead_ops);
    }
  }
}

void RemoveVariables(llvm::ArrayRef<VarHandleOp> vars) {

          assert(user->use_empty());
          user->erase();
        }
      }
    }

    for (auto operand : cluster.getOperands()) {
      mlir::Operation* def = operand.getDefiningOp();
      if (operand.hasOneUse() &&
          llvm::isa_and_nonnull<mlir::TF::TPUPartitionedInputV2Op>(def)) {
        operand.dropAllUses();
        def->erase();
      }
    }

};

// Returns the first value in the chain of operands, which is not defined by a
// tf.IdentityOp or a tf.ReadVariableOp.
Value SkipIdentityAndReadVariable(Value v) {
  while (auto op = v.getDefiningOp()) {
    if (!isa<TF::IdentityOp, TF::ReadVariableOp>(op)) break;
    v = op->getOperand(0);
  }
  return v;
}

void AnnotateParameterReplicationPass::runOnOperation() {
  ModuleOp m = getOperation();

        !result_type || !result_type.getElementType().isSignlessInteger(8)) {
      return failure();
    }

    auto input_op =
        dyn_cast<arith::ConstantOp>(op.getOperand(0).getDefiningOp());
    if (!input_op) {
      return failure();
    }

    Builder builder(op.getContext());
    auto new_gather_op = rewriter.create<TFL::GatherOp>(
        op.getLoc(),
        /*result=*/

  Value cond_block_argument = cond_block.getArgument(idx);
  // Consider the op that defines the unused result as a candidate for pruning.
  Operation* candidate_op = body_yield_operand.getDefiningOp();
  if (candidate_op == nullptr) return false;

  // Don't prune if candidate op might have side effects.
  if (isa_and_nonnull<TF::TensorFlowDialect>(candidate_op->getDialect())) {