val request2 = Request.Builder().url(server.url("/")).build()
    val response2 = client.newCall(request2).execute()
    assertThat(response2.body.string()).isEqualTo("def")

    // The HTTP calls should share a single connection.
    assertThat(socksProxy.connectionCount()).isEqualTo(1)
  }

  @Test
  fun proxySelector() {
    server.enqueue(MockResponse.Builder().body("abc").build())
    val proxySelector: ProxySelector =

 * prefix are called a 'section'. There are 128 code points per section.
 *
 * Ranges Data (32,612 bytes)
 * ==========================
 *
 * Each entry is 4 bytes, and represents a _range_ of code points that all share a common 14-bit
 * prefix. Entries are sorted by their complete code points.
 *
 * The 4 bytes are named b0, b1, b2 and b3. We also define these supplemental values:
 *
 *  * **b2a**: b2 + 0x80
 *  * **b3a**: b3 + 0x80

                                                + " Your JAR contains rogue Maven Plugin metadata."
                                                + " Possible causes may be: shaded into this JAR some Maven Plugin or some rogue resource.",
                                        artifact.getGroupId(),
                                        artifact.getArtifactId(),

  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.

            log.debug("Adding key " + key + " to " + dr);
        }

        /*
         * Subtract the server and share from the pathConsumed so that
         * it reflects the part of the relative path consumed and not
         * the entire path.
         */
        dri.stripPathConsumed(1 + server.length() + 1 + share.length());

        if ( key.charAt(key.length() - 1) != '\\' ) {
            key += '\\';
        }

  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);
  TFE_OpSetAttrString(op, "container", "localhost", 0);
  TFE_OpSetAttrString(op, "shared_name", "", 0);
  if (!device_name.empty()) {
    TFE_OpSetDevice(op, device_name.c_str(), status);
  }

  void GetTensor(const string& name,
                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,

    assertThat(a.dispatcher).isNotNull()
    assertThat(a.connectionPool).isNotNull()
    assertThat(a.sslSocketFactory).isNotNull()
    assertThat(a.x509TrustManager).isNotNull()

    // Multiple clients share the instances.
    val b = client.newBuilder().build()
    assertThat(b.dispatcher).isSameAs(a.dispatcher)
    assertThat(b.connectionPool).isSameAs(a.connectionPool)

    /**
     * This default mode for combining children DOMs during merge means that where element names match, the process will
     * try to merge the element data, rather than putting the dominant and recessive elements (which share the same
     * element name) as siblings in the resulting DOM.
     */
    String DEFAULT_CHILDREN_COMBINATION_MODE = CHILDREN_COMBINATION_MERGE;

    String SELF_COMBINATION_MODE_ATTRIBUTE = "combine.self";

TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s,
                  const char* name = "neg");

TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s);

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,