struct TF_Tensor, class Tensor

Struct TF_Tensor is defined in c_api_internal.h

struct TF_Tensor {
  ~TF_Tensor();

  TF_DataType dtype;
  tensorflow::TensorShape shape;
  tensorflow::TensorBuffer* buffer;
};

It's simple compares to the definition to class Tensor which is defined in tensorflow/core/framework/tensor.h

/// @ingroup core
/// Represents an n-dimensional array of values.
class Tensor {
 public:
  /// \brief Creates a 1-dimensional, 0-element float tensor.
  ///
  /// The returned Tensor is not a scalar (shape {}), but is instead
  /// an empty one-dimensional Tensor (shape {0}, NumElements() ==
  /// 0). Since it has no elements, it does not need to be assigned a
  /// value and is initialized by default (IsInitialized() is
  /// true). If this is undesirable, consider creating a one-element
  /// scalar which does require initialization:
  ///
  /// ```c++
  ///
  ///     Tensor(DT_FLOAT, TensorShape({}))
  ///
  /// ```
  Tensor();

  /// \brief Creates a Tensor of the given `type` and `shape`.  If
  /// LogMemory::IsEnabled() the allocation is logged as coming from
  /// an unknown kernel and step. Calling the Tensor constructor
  /// directly from within an Op is deprecated: use the
  /// OpKernelConstruction/OpKernelContext allocate_* methods to
  /// allocate a new tensor, which record the kernel and step.
  ///
  /// The underlying buffer is allocated using a `CPUAllocator`.
  Tensor(DataType type, const TensorShape& shape);


...


  // Only needed by variable op to set the shape of an uninitialized
  // Tensor.
  // TODO: Remove this when we have a better story for detecting
  // uninitialized tensors.
  void set_shape(const TensorShape& shape) {
    DataType dt = dtype();
    shape_ = shape;
    set_dtype(dt);
  }

  void CopyFromInternal(const Tensor& other, const TensorShape& shape);

  template <typename T>
  T* base() const;

  template <size_t NDIMS>
  void FillDimsAndValidateCompatibleShape(
      gtl::ArraySlice<int64> new_sizes,
      Eigen::array<Eigen::DenseIndex, NDIMS>* dims) const;

  template <typename T, size_t NDIMS>
  void FillDimsAndValidateCompatibleShape(
      gtl::ArraySlice<int64> new_sizes,
      Eigen::array<Eigen::DenseIndex, NDIMS>* dims) const;
};

}


Since a class has many functions , those will be a function pointer at the virtual function table, don't know where that is.  But once TF_Tensor is changed, class Tensor needs to be changed too.

Member functions besides, I really can't match the member variables in class Tensor with struct TF_Tensor.  There are tensorflow::TensorShape shape;  tensorflow::TensorBuffer* buffer;

no where to find TF_DataType dtype;

If I want to add a new member variable, I can put it at the end, it works.


They do have differences, from the comments in TF_NewTensor()


    // TF_STRING and TF_RESOURCE tensors have a different representation in
    // TF_Tensor than they do in tensorflow::Tensor. So a copy here is a waste
    // (any alignment requirements will be taken care of by TF_TensorToTensor
    // and TF_TensorFromTensor).
    //
    // Other types have the same representation, so copy only if it is safe to
    // do so.