return failure();
    }

    // The input tensors x and y are 2-D or higher with shape:
    //       [..., r_x == 1, c_x] and [..., c_y, r_y].
    // The position of r_* and c_* are determined by the polarity of
    // the adj(X|Y) attribute, respectively.
    // So adjX == True indicates [..., c_x, r_x == 1].
    llvm::ArrayRef<int64_t> lhs_shape =
        mlir::cast<RankedTensorType>(bmm_op.getX().getType()).getShape();

//   * size(batching_dimensions) <= 3 (TFLite support restriction)
//   * size(contracting_dimensions) = 1
//   * Input tensors are per-tensor uniform quantized (i8->f32)
//     tensors (full integer) with shape [..., r_x, c_x] or [..., c_x, r_x].
//   * The filter tensor is a per-tensor uniform quantized (i8->f32) tensor
//     (constant or activation) with shape [..., r_y, c_y] or [..., c_y, r_y].

	// Use WithoutCancel instead of a new context.TODO() to propagate trace context
	// Call the container runtime's SyncPod callback
	sctx := context.WithoutCancel(ctx)
	result := kl.containerRuntime.SyncPod(sctx, pod, podStatus, pullSecrets, kl.backOff)
	kl.reasonCache.Update(pod.UID, result)
	if err := result.Error(); err != nil {
		// Do not return error if the only failures were pods in backoff

      return signalPassFailure();
    } else if (def_op == backward_caller) {
      loop_body_func->emitOpError(
          "Unexpected loop carried variable dependency on sc_");
      return signalPassFailure();
    } else if (llvm::isa<BlockArgument>(operand)) {
      // pass
    } else {
      // This should never happen.

the `adj_x` or `adj_y` flag to `True`, which are by default `False`.

The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`
and `[..., r_y, c_y]`.

The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where:

    r_o = c_x if adj_x else r_x
    c_o = r_y if adj_y else c_y

It is computed as:

    output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])