Type qtype = attr.getValue();
          auto tensor_or = BuildTensorFromType(
              qtype, name_mapper_.GetUniqueName(intermediate).str());
          if (!tensor_or.has_value()) {
            continue;
          } else {
            intermediates.push_back(tensors.size());
            tensors.push_back(tensor_or.value());
          }
        }
      }
    }

// Note: Keep in sync with exported copy of enum in eager/c_api.h.
enum ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  DEVICE_PLACEMENT_WARN = 1,
  // Silently copy the tensor, which has a performance cost since the operation
  // will be blocked till the copy completes. This is the default policy.

  return %0 : tensor<1x1x1xf32>
}

//CHECK:func.func private @slice(%arg0: tensor<160x20x1xf32>) -> tensor<1x1x1xf32> {

    for (const auto& iter : specs.inputs) {
      TensorId tensor_id = ParseTensorName(iter.first);
      feed_fetch_nodes.insert(std::string(tensor_id.node()));
    }
    for (const auto& output : llvm::concat<const std::string>(
             specs.outputs, specs.control_outputs)) {
      TensorId tensor_id = ParseTensorName(output);
      feed_fetch_nodes.insert(std::string(tensor_id.node()));
    }

        ) from ex


def _convert_values_to_tf_tensors(
    sample: rd.RepresentativeSample,
) -> Mapping[str, core.Tensor]:
  """Converts TensorLike values of `sample` to Tensors.

  Creates a copy of `sample`, where each value is converted to Tensors
  unless it is already a Tensor.
  The values are not converted in-place (i.e. `sample` is not mutated).

  Args:

  // 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));

  %0 = "tf.TensorListFromTensor"(%arg0, %arg1) : (tensor<*xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<*xf32>>>
  %1 = "tf.TensorListGetItem"(%0, %arg2, %arg1) : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<1xi32>) -> tensor<*xf32>
  %2 = "tf.TensorListStack"(%0, %arg1) : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<1xi32>) -> tensor<*xf32>
  func.return %1, %2 : tensor<*xf32>, tensor<*xf32>

def TFR_CastOp : TFR_Op<"cast", [Pure]> {
  let description = [{
    The `cast` operation converts the operand with built-in tensor type to
    tfr.tensor type, or vice versa.

    Example:

    ```mlir
    %1 = tfr.cast(%0) : tensor<f32> -> !tfr.tensor
    %3 = tfr.cast(%1) : !tfr.tensor -> tensor<f32>
    ```
  }];

  let arguments = (ins TFR_singleTensorType:$arg);

    Note: Additional result corresponds to the control output.
  }];

  let arguments = (ins
    TensorOf<[I1]>:$input,
    Variadic<TfeControlType>:$controlInputs
  );
  let results = (outs
    TensorOf<[I1]>:$output,
    TfeControlType:$control
  );

  let hasVerifier = 0;
}

func.func @main(tensor<4x4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4x4xf32> {
// CHECK: {
// CHECK-NEXT:   version: 3,