allocate tensor

TF_Tensor* TF_AllocateTensor(TF_DataType dtype, const int64_t* dims,
                             int num_dims, size_t len) {
  void* data = allocate_tensor("TF_AllocateTensor", len);
  return TF_NewTensor(dtype, dims, num_dims, data, len, deallocate_buffer,
                      nullptr);
}

TF_Tensor* TF_NewTensor(TF_DataType dtype, const int64_t* dims, int num_dims,
                        void* data, size_t len,
                        void (*deallocator)(void* data, size_t len, void* arg),
                        void* deallocator_arg) {
  std::vector<tensorflow::int64> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<tensorflow::int64>(dims[i]);
  }

  TF_ManagedBuffer* buf = new TF_ManagedBuffer;
  buf->len_ = len;
  if (dtype != TF_STRING && dtype != TF_RESOURCE &&
      tensorflow::DataTypeCanUseMemcpy(static_cast<DataType>(dtype)) &&
      reinterpret_cast<intptr_t>(data) % EIGEN_MAX_ALIGN_BYTES != 0) {
    // 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.
    buf->data_ = allocate_tensor("TF_NewTensor", len);
    std::memcpy(buf->data_, data, len);
    buf->deallocator_ = deallocate_buffer;
    buf->deallocator_arg_ = nullptr;
    // Free the original buffer.
    deallocator(data, len, deallocator_arg);
  } else {
    buf->data_ = data;
    buf->deallocator_ = deallocator;
    buf->deallocator_arg_ = deallocator_arg;
  }
  TF_Tensor* ret = new TF_Tensor{dtype, TensorShape(dimvec), buf};
  size_t elem_size = TF_DataTypeSize(dtype);
  if (elem_size > 0 && len < (elem_size * ret->shape.num_elements())) {
    delete ret;
    return nullptr;
  }
  return ret;
}