// Note: The caller must ensure to check that the dtypes and sizes of the
// underlying buffers are the same before calling this.
// dtype must be any integer dtype, float, or double.
void CheckBufferDataIsEqual(DataType dtype, int64_t num_elements, void* a,
                            void* b);

// Converts a TensorHandle to a Tensor, and dies if unsuccessful. This should

#include "tensorflow/c/experimental/saved_model/core/test_utils.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/eager/context.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"

    // 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;

  %1 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

  func.return %24 : tensor<1x4xf32>

  func.return %24 : tensor<1x4xf32>

    auto get_num_elements = [](RankedTensorType tensor) {
      int num_elements = 1;
      for (int i = 0; i < tensor.getRank(); ++i) {
        // Assume dynamic dim size as the dim size one.
        if (!tensor.isDynamicDim(i)) {
          num_elements *= tensor.getDimSize(i);
        }
      }
      return num_elements;
    };

// been converted to constants, all the assign ops are gone, etc.). However,
// TFLite has these variable tensors semantics. So the variable mapping from TF
// to TFLite is actually broken here, we sort of hard-code the variable tensors
// based on the actual ops using them, such as unidirectional_sequence_lstm.
//
// MLIRConverter also benefits from lots of typical compiler optimization like