///
  /// Similar to `rename_file`, but both `src` and `dst` exist after this call
  /// with the same contents. In case of error, both `src` and `dst` keep the
  /// same state as before the call.
  ///
  /// If `dst` is a directory, creates a file with the same name as the source
  /// inside the target directory.
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if rename was completed.

  fi
fi

# Build for both JAX and TF usage.  We do these in one place because they share
# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG"
  docker pull "$IMAGE" || true
  # Due to some flakiness of resources pulled in the build, allow the docker
  # command to reattempt build a few times in the case of failure (b/302558736)

    `legacy_parse_example` (same for related functions).
*   New `Variable`s are not added to the same collection several times even if a
    list with duplicates is passed to the constructor.
*   The Python API will now properly set the `list` member of `AttrValue` in
    constructed `GraphDef` messages for empty lists. The serialization of some

// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).
// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {
 protected:
  enum AbstractContextKind { kGraph, kMlir, kEager, kTfrt, kTape, kOpHandler };

      if (existing != accumulated_gradients_.end()) {
        // This is a somewhat odd case to be in, since it means we have two
        // operations which supposedly both created the same Tensor. It comes up
        // in recompute_grad, where the gradients have the same value. However,
        // only the original gradient is connected to everything else, so we
        // should still use that.
        vspace_.DeleteGradient(forward_grads[i]);

      options:
        - "Yes"
        - "No"
    validations:
      required: true

  - type: markdown
    attributes:
      value: |

//
// Typical status codes:
//  * TF_OK - dirname exists and we were able to delete everything underneath
//  * TF_NOT_FOUND - dirname doesn't exist
//  * TF_PERMISSION_DENIED - dirname or some descendant is not writable
//  * TF_UNIMPLEMENTED - some underlying functions (like Delete) are not
//    implemented
TF_CAPI_EXPORT extern void TF_DeleteRecursively(const char* dirname,

  //
  // Otherwise if all of the tensors have the same shape, returns that via the
  // `shape` output argument. This blocks waiting for async tensors, may return
  // a delayed bad status encountered during async execution, and will return a
  // bad status unless all tensors have the same shape.
  Status Shape(const std::vector<int64_t>** shape) const;

// `len`. The format is subject to change in the future.
// The returned string is heap-allocated, and caller should call free() on it.
//
// Do not return const char*, because some foreign language binding
// (e.g. swift) cannot then call free() on the returned pointer.
TF_CAPI_EXPORT extern char* TF_FunctionDebugString(TF_Function* func,
                                                   size_t* len);

      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;