# MLIR-NEXT:    return %[[add]] : tensor<4x!quant.uniform<u8:f32, 0.0078431372549019607:128>>
# MLIR-NEXT:  }

# CHECK-LABEL: {
# CHECK-NEXT:  version: 3,
# CHECK-NEXT:  operator_codes: [ {

  void CopyCPUTensorToDevice(const Tensor* cpu_tensor, Device* device,
                             Tensor* device_tensor, StatusCallback done,
                             bool sync_dst_compute) const override;
  void CopyDeviceTensorToCPU(const Tensor* device_tensor,
                             absl::string_view tensor_name, Device* device,
                             Tensor* cpu_tensor, StatusCallback done) override;

// drop below the capacity due to dequeuing.
//
// Tensors are dequeued via the corresponding TF dequeue op.
// TODO(hongm): Add support for `timeout_ms`.
TF_CAPI_EXPORT extern void TF_EnqueueNamedTensor(TF_Session* session,
                                                 int tensor_id,
                                                 TF_Tensor* tensor,

      << "Tensor creation failed for tensor of dtype: "
      << DataTypeString(dtype);
  CHECK_EQ(tensor->Type(), dtype);
  for (int i = 0; i < shape.size(); ++i) {
    CHECK_EQ(tensor->Dim(i), shape[i]);
  }
  FillNumericTensorBuffer(tensor->Type(), tensor->NumElements(), tensor->Data(),
                          value);
  ImmediateTensorHandlePtr handle(ctx->CreateLocalHandle(tensor.get()));
  CHECK_NE(handle.get(), nullptr);

        ) 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:

  // Tensors: Vector of tensors that back the TensorValue inputs
  // Inputs: Vector of inputs that are backed by tensors.
  mlir::LogicalResult PrepareKernelInputs(
      const llvm::SmallDenseSet<int>& required_consts,
      std::vector<tensorflow::XlaExpression>& expressions,
      std::vector<tensorflow::Tensor>& tensors,
      std::vector<tensorflow::TensorValue>& inputs);

    // op is used to retrieve the tensor values of each variable. See
    // `InitializeVariablesInSessionInitializer` for further details.
    const auto loc = NameLoc::get(StringAttr::get(
        var_handle_op.getContext(), var_handle_op.getSharedName()));
    var_handle_op->setLoc(loc);

    // Ex) If VarHandleOp's type is tensor<!tf_type.resource<tensor<1xf32>>>,
    // then tensor<1xf32> is the subtype.

// Sets the allows_uninitialized_input property of the operation built by this
// builder.
//
// By default, all inputs to an Op must be initialized Tensors. Ops that may
// initialize tensors for the first time should set this field to true, to allow
// the Op to take an uninitialized Tensor as input.
TF_CAPI_EXPORT extern void TF_OpDefinitionBuilderSetAllowsUninitializedInput(
    TF_OpDefinitionBuilder* builder, bool allows_uninitialized_input);

  string name;

  // List of args (either as a TensorTypeAndShape or as a Tensor value)
  // for compile-time constant arguments to the compilation, ordered by
  // argument number. Tensors must be in host memory.
  using TensorTypeAndShape =
      std::pair<DataType, absl::InlinedVector<int64_t, 4>>;
  absl::InlinedVector<std::variant<Tensor, TensorTypeAndShape>, 8> args;

  func.return %24 : tensor<1x4xf32>