}

// Return a tensor handle containing a 100x100 matrix of floats
TFE_TensorHandle* TestMatrixTensorHandle100x100(TFE_Context* ctx);

// Return a tensor handle containing a 3x2 matrix of doubles
TFE_TensorHandle* DoubleTestMatrixTensorHandle3X2(TFE_Context* ctx);

// Return a tensor handle containing a 3x2 matrix of floats
TFE_TensorHandle* TestMatrixTensorHandle3X2(TFE_Context* ctx);

  // dtype of read operations).
  TF_DataType type_;
};

// Creates a TFE_TensorHandle with value `v`.
TensorHandlePtr FloatTensorHandle(float v, TF_Status* status);

// Creates a rank-one TFE_TensorHandle with value `v`.
TensorHandlePtr VectorFloatTensorHandle(const std::vector<float>& v,
                                        TF_Status* status);

// Helper to un-pack `num_replicas` TFE_TensorHandles from one parallel handle.

TF_CAPI_EXPORT extern void TF_SetAttrFloat(TF_OperationDescription* desc,
                                           const char* attr_name, float value);
TF_CAPI_EXPORT extern void TF_SetAttrFloatList(TF_OperationDescription* desc,
                                               const char* attr_name,
                                               const float* values,
                                               int num_values);

  Status SetAttrString(const char* attr_name, const char* data,
                       size_t length) override;
  Status SetAttrInt(const char* attr_name, int64_t value) override;
  Status SetAttrFloat(const char* attr_name, float value) override;
  Status SetAttrBool(const char* attr_name, bool value) override;
  Status SetAttrType(const char* attr_name, DataType value) override;
  Status SetAttrShape(const char* attr_name, const int64_t* dims,

};

// Template instantiations here

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:
      return true;
    case DT_QINT8:
    case DT_QINT16:
    case DT_QINT32:

                               size_t length) = 0;
  virtual Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual Status SetAttrType(const char* attr_name, DataType value) = 0;
  virtual Status SetAttrShape(const char* attr_name, const int64_t* dims,

    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error = 1e-2);

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error = 1e-2);

Model BuildGradModel(Model forward, GradientRegistry registry);

}  // namespace internal
}  // namespace gradients

                                            int64_t value);
TF_CAPI_EXPORT extern void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name,
                                              float value);
TF_CAPI_EXPORT extern void TFE_OpSetAttrBool(TFE_Op* op, const char* attr_name,
                                             unsigned char value);

Status SetAttrInt(AbstractOperation*, const char* attr_name, int64_t value,
                  ForwardOperation*);
Status SetAttrFloat(AbstractOperation*, const char* attr_name, float value,
                    ForwardOperation*);
Status SetAttrBool(AbstractOperation*, const char* attr_name, bool value,
                   ForwardOperation*);

// Create 1 dimensional tensor with values from `values`
TF_Tensor* Int32Tensor(const std::vector<int32_t>& values);

TF_Tensor* Int32Tensor(int32_t v);

TF_Tensor* DoubleTensor(double v);

TF_Tensor* FloatTensor(float v);

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,