// There should be 3 tensors: input, output, and BroadcastTo/shape.
  EXPECT_THAT(subgraph->tensors, SizeIs(3));

  // Input Tensor
  EXPECT_THAT(subgraph->tensors[0]->type, Eq(tensor_type_));
  EXPECT_THAT(subgraph->tensors[0]->name, Eq("input_1"));
  EXPECT_THAT(subgraph->tensors[0]->quantization->scale, SizeIs(1));
  EXPECT_THAT(subgraph->tensors[0]->quantization->zero_point, SizeIs(1));

// 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

SparseTensor is not supported. The return value of the decorated function
must be a Tensor or a list/tuple of Tensors.
  }];

  let arguments = (ins
    Arg<Variadic<TF_Tensor>, [{The tensors to be batched.}]>:$in_tensors,
    Arg<Variadic<TF_Tensor>, [{The tensors which are captured in the function, and don't need
to be batched.}]>:$captured_tensors,

    SymbolRefAttr:$f,
    I64Attr:$num_batch_threads,

    }

    // Extract individual tensor list element and combine them using the tf.Pack
    // op.
    Location loc = op.getLoc();
    llvm::SmallVector<Value, 4> values;
    values.reserve(tensors.size());
    for (const tensorflow::Tensor &tensor : tensors) {
      auto attr_or = tensorflow::ConvertTensor(tensor, &rewriter);
      if (!attr_or.ok()) return failure();

      }
      return %graph#0, %graph#1, %graph#2, %graph#3 : !tf_res, !tf_res, tensor<f32>, tensor<f32>
    }
    ```

    After:

    ```mlir
    func @while_body(%arg0: !tf_res, %arg1: !tf_res, %arg2: tensor<f32>, %arg3: tensor<f32>, %chain_0: tensor<i32>, %chain_1: tensor<i32>) -> (!tf_res, !tf_res, tensor<f32>, tensor<f32>, tensor<i32>, tensor<i32>) {

      return tflite::CreateBuffer(builder_, buffer_data);
    }
  }

  tensorflow::Tensor tensor;
  auto status = tensorflow::ConvertToTensor(attr, &tensor);
  if (!status.ok()) {
    inst->emitError(
        Twine("failed to convert value attribute to tensor with error: " +
              status.ToString()));
    return std::nullopt;
  }

  return
}
func.func private @false(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {
  return %arg0 : tensor<!tf_type.variant<tensor<f32>>>
}
func.func private @true(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {
  return %arg0 : tensor<!tf_type.variant<tensor<f32>>>
}

// -----

*  `tf.Tensor`

//        start_indices = dense<0> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x1xf32>
// %1 = "mhlo.slice"(%input) {
//        limit_indices = dense<[4, 3]> : tensor<2xi64>,
//        start_indices = dense<[0, 1]> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>

  %2 = "tfl.add"(%arg0, %arg3) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %3 = "tfl.pack"(%1, %2) {axis = 0 : i32, per_device_costs = {CPU = 2.0 : f32, GPU = -1.0 : f32}, values_count = 2 : i32, tac.device = "CPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>