gcloud compute firewall-rules create "${CLUSTER_NAME}-default-internal-node" \
      --project "${NETWORK_PROJECT}" \
      --network "${NETWORK}" \
      --source-ranges "10.0.0.0/8" \
      --allow "tcp:1-65535,udp:1-65535,icmp" \
      --target-tags "${NODE_TAG}"&
  fi

  if ! gcloud compute firewall-rules describe --project "${NETWORK_PROJECT}" "${NETWORK}-default-ssh" &>/dev/null; then

  SameOperandsAndResultShape]> {
  let summary = "Computes the complex absolute value of a tensor.";

  let description = [{
Given a tensor `x` of complex numbers, this operation returns a tensor of type
`float` or `double` that is the absolute value of each element in `x`. All
elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
value is computed as \\( \sqrt{a^2 + b^2}\\).
  }];

		// {ADD,SUB,MUL,DIV,AND,OR,XOR}x: binary integer ops
		//   unadorned versions compute arg0 OP arg1
		//       const versions compute arg0 OP auxint (auxint is a sign-extended 32-bit value)
		// constmodify versions compute *(arg0+ValAndOff(AuxInt).Off().aux) OP= ValAndOff(AuxInt).Val(), arg1 = mem

t=this.$options.methods;if(t)for(var e in t)this[e]=t[e].bind(this)},Ki.prototype._initComputeds=function(){var t=this.$options.computed;if(this._computeds={},t)for(var e in t)rn(this,e,t[e])},Ki.prototype._initWatches=function(){var t=this.$options.computed;if(t)for(var e in t){var i=t[e],n=i.watch,r=i.immediate;n&&r&&n.call(this,this[e])}},Ki.prototype._callWatches=function(){var t=this.$options.computed,e=this._computeds;for(var i in e){var n=e[i];delete e[i],t[i].watch&&!U(n,this[i])&&t[i].watch.call(this,this...

data.offsets.reference,\n    this.popper,\n    this.reference,\n    this.options.modifiers.flip.boundariesElement,\n    this.options.modifiers.flip.padding\n  );\n\n  // store the computed placement inside `originalPlacement`\n  data.originalPlacement = data.placement;\n\n  data.positionFixed = this.options.positionFixed;\n\n  // compute the popper offsets\n  data.offsets.popper = getPopperOffsets(\n    this.popper,\n    data.offsets.reference,\n    data.placement\n  );\n\n  data.offsets.popper.position =...

  if (!input_ty.hasStaticShape()) {
    output_ty =
        tensorflow::GetTypeFromTFTensorShape(output_ty_shape, element_ty);
    return success();
  }

  // Compute the value of the unknown dimension.
  if (unknown_index != -1) {
    // Compute number of elements in tensor shape.
    int64_t input_ty_size = 1;
    bool input_ty_zero_dim = false;
    for (const auto& dim : input_ty.getShape()) {

                                   ModuleOp module);

  // Tries to compute the result of folding the op. This doesn't actually
  // perform constant folding, it is just computes the equivalent constants.
  // Returns whether it was able to compute constant values.
  LogicalResult TryToFold(Operation* op);

  // Forcely assign operand types to result types (the i-th operand type will

      // meant that sometimes a too-large threshold is calculated. However, this new threshold is
      // independent of the initial capacity, except that it won't be lower than the threshold
      // computed from that capacity. Because the internal table is only allocated on the first
      // write, we won't see copying because of the new threshold. So it is always OK to use the
      // calculation here.

        list.add(closeable, closingExecutor);
      }
      return closeable;
    }
  }

  /**
   * An operation that computes a result.
   *
   * @param <V> the type of the result
   */
  public interface ClosingCallable<V extends @Nullable Object> {
    /**
     * Computes a result, or throws an exception if unable to do so.
     *

  }

  @Override
  @CheckForNull
  public V compute(
      K key, BiFunction<? super K, ? super @Nullable V, ? extends @Nullable V> function) {
    checkNotNull(key);
    checkNotNull(function);
    int hash = hash(key);
    return segmentFor(hash).compute(key, hash, function);
  }

  @Override