return absl::OkStatus();
  }

  // We may need to allocate zero inputs for trainable dtypes we don't have JVPs
  // for. Make sure they get cleaned up.
  std::vector<Gradient*> new_zeros;
  auto delete_new_zeros = gtl::MakeCleanup([&new_zeros, this] {
    for (Gradient* tensor : new_zeros) {
      this->vspace_.DeleteGradient(tensor);
    }
  });
  std::vector<Gradient*> in_grads;

    }
  }

  TF_RETURN_IF_ERROR(GradientTape::ComputeGradient(
      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,
      output_gradients, result, /*build_default_zeros_grads*/ false));
  return absl::OkStatus();
}

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.

  void* data = dl_tensor->data;

  if (dl_tensor->byte_offset != 0) {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported byte_offset (", dl_tensor->byte_offset,
        ") from DLPack, must be zero");
    return nullptr;
  }

  size_t total_bytes = dl_tensor->dtype.bits / 8;
  for (int i = 0; i < num_dims; i++) {
    total_bytes *= dims[i];
  }

  if (dl_tensor->strides != nullptr &&

-	}
-      n = n - (s1 - s) - 1;
-      if (n == 0)
-	return s;
-      goto zero_fill;
-    }
-
- last_chars:
-  n &= 3;
-  if (n == 0)
-    return s;
-
-  do
-    {
-      c = *s2++;
-      *++s1 = c;
-      if (--n == 0)
-	return s;
-    }
-  while (c != '\0');
-
- zero_fill:
-  do
-    *++s1 = '\0';
-  while (--n > 0);
-
-  return s;