}

               @Test
               @Category(Slow.class)
               public void slowTest() {
                  System.out.println(System.getProperty("java.version"));
                  Assert.assertEquals(1,1);
               }

               interface Fast {}
               interface Slow {}
            }
        """.stripIndent()
    }

      %1:2 = "tf.IdentityN"(%arg0, %arg1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")
      %2:2 = "tf.IdentityN"(%1#0, %1#1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")
      tf_executor.yield %2#0, %2#1 : tensor<i32>, tensor<f32> loc("identity@some_function")
    } loc("identity@some_function")
    tf_executor.fetch
  }
  func.return

        loc, ArrayRef<Type>{slice_type},
        ArrayRef<Value>{element, GetR1Const(slice_shape, builder, loc)});
  }
  return builder
      .create<TF::XlaDynamicUpdateSliceOp>(
          loc, ArrayRef<Type>{buffer.getType()},
          ArrayRef<Value>{buffer, update_slice,
                          GetIndicesForElement(index, buffer, builder, loc)})
      .getOutput();
}

    %6:2 = tf_executor.LoopCond %5#0 : (tensor<*xi1>) -> (tensor<*xi1>, !tf_executor.control) {device = ""} loc("while/LoopCond")
    %7:3 = tf_executor.Switch %3#0, %6#0 : (tensor<*xi32>, tensor<*xi1>) -> (tensor<*xi32>, tensor<*xi32>, !tf_executor.control) {device = "", T = "tfdtype$DT_INT32", _class = ["loc:@while/Merge"]} loc("while/Switch")

        loc, broadcasted_input_type, input,
        Create1DConstValue(builder, loc, broadcasted_shapes_or->first));
    weight = builder.create<TF::BroadcastToOp>(
        loc, broadcasted_weight_type, weight,
        Create1DConstValue(builder, loc, broadcasted_shapes_or->second));
    return;
  }

  const Value zero = Create1DConstValue<int32_t>(builder, loc, {0});
  const Value num_matmul_dim_value =

    # Basic check that the debug info file is being correctly saved and loaded.
    #
    # CHECK: "tf.AddV2"{{.*}}loc(#loc{{[0-9]+}})
    # CHECK: "tf.Identity"{{.*}}loc(#loc{{[0-9]+}})
    # CHECK: #loc{{[0-9]+}} = loc("{{.*}}debug_info.py":{{[0-9]+}}:{{[0-9]+}})
    # CHECK: #loc{{[0-9]+}} = loc(callsite(#loc{{[0-9]+}} at #loc{{[0-9]+}}))


if __name__ == '__main__':

	//    <g id="graph0" transform="...">
	//    ...
	//    </g>
	//    </g>
	//    </svg>

	if loc := viewBox.FindStringIndex(svg); loc != nil {
		svg = svg[:loc[0]] +
			`<svg width="100%" height="100%"` +
			svg[loc[1]:]
	}

	if loc := graphID.FindStringIndex(svg); loc != nil {
		svg = svg[:loc[0]] +
			`<script type="text/ecmascript"><![CDATA[` + svgpan.JSSource + `]]></script>` +

// Creates an 1D array const with float values.
class Create1DConst<string values> : NativeCodeCall<
  "Create1DConstValue<float>($_builder, $_loc, "# values #")">;

// Creates a scalar const with float value.
class CreateScalarConst<string value> : NativeCodeCall<
  "CreateScalarConstValue<float>($_builder, $_loc, "# value #")">;

// Creates an 1D array const with integer values.

       : (tensor<1x3x4x3xbf16>, tensor<2x3x3x2xbf16>) -> tensor<1x3x2x2xbf16> loc("Model/conv2d")
  %2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x3x2x2xbf16>) -> tensor<1x3x2x2xf32>
  %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
  return %3 : tensor<1x3x2x2xf32>
// CHECK: tf.Conv2D
// CHECK-SAME: loc("Model/conv2d")
}

                    "type": "missing",
                    "loc": ["query", "token"],
                    "msg": "Field required",
                    "input": None,
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["query", "token"],
                    "msg": "field required",