{
          "shape": [1, 4],
          "name": "input_activation_state18",
          "is_variable": true,
          "quantization": {
            "min": [-0.8],
            "max": [1.6]
          }
        },
        {
          "shape": [1, 2],
          "name": "input_cell_state19",
          "is_variable": true,
          "quantization": {
            "min": [-2.0],
            "max": [4.0]

// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>

// CHECK-DAG: %[[RES0:.*]] = "tfl.pseudo_const"() <{value = dense<{{.*}}> : tensor<1x4xf32>}> {tfl.is_variable} : () -> tensor<1x4xf32>
// CHECK-DAG: %[[RES1:.*]] = "tfl.pseudo_const"() <{value = dense<{{.*}}> : tensor<1x4xf32>}> {tfl.is_variable} : () -> tensor<1x4xf32>

            return LogicalResult::failure();
          }

          if (is_variable) {
            input.emitOpError() << "unexpected multiple TPUReplicatedInputOp "
                                << "for single argument";
            return LogicalResult::failure();
          }
          is_variable = true;
        } else {
          is_non_variable = true;
        }
      }

                                  const std::vector<uint8_t>& buffer,
                                  bool is_variable, OpBuilder builder,
                                  Location loc, bool use_stablehlo_constant) {
  if (tensor.sparsity != nullptr) {
    return BuildSparseConstOp(tensor, buffer, builder, loc);
  }

  if (is_variable) {
    return BuildVariableOp(tensor, builder, loc);
  }

  // marked as a stateful. If so, set the tensor's is_variable as true
  // This is v1 ref variable semantics in the TFLite runtime.
  bool is_variable = false;
  for (auto& use : value.getUses()) {
    is_variable = IsStatefulOperand(use.getOwner(), use.getOperandNumber());
    if (is_variable) {
      break;
    }
  }

// as tensor names may have changed via MLIR constant folding process.
bool ExpectEqualTensor(const Tensor* tensor, const Tensor* expected_tensor) {
  return (tensor->is_variable() == expected_tensor->is_variable()) &&
         (GetAsVector(tensor->shape()) ==
          GetAsVector(expected_tensor->shape()));
}

// Finds the match of the quantized tensor from the possible tensors. Each

            return LogicalResult::failure();
          }

          if (is_variable) {
            input.emitOpError() << "unexpected multiple TPUReplicatedInputOp "
                                << "for single argument";
            return LogicalResult::failure();
          }
          is_variable = true;
        } else {
          is_non_variable = true;
        }
      }

};

void ExpectEqualTensor(TensorT* tensor, TensorT* expected_tensor) {
  const float eps = 1e-7;
  EXPECT_THAT(expected_tensor, NotNull());
  EXPECT_THAT(tensor->is_variable, Eq(expected_tensor->is_variable));
  EXPECT_THAT(tensor->shape, Eq(expected_tensor->shape));
  EXPECT_THAT(tensor->type, Eq(expected_tensor->type));
  const auto quantization_params = tensor->quantization.get();

    fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization);
  }
  void add_is_variable(bool is_variable) {
    fbb_.AddElement<uint8_t>(Tensor::VT_IS_VARIABLE, static_cast<uint8_t>(is_variable), 0);
  }
  void add_sparsity(::flatbuffers::Offset<tflite::SparsityParameters> sparsity) {
    fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity);
  }