%0 = "tfl.shape"(%arg1) : (tensor<8x7x6x5x?x3x2x1xf32>) -> tensor<8xi64>
  %1 = "tfl.shape"(%arg2) : (tensor<?x3x2x1xf32>) -> tensor<4xi64>
  %2 = "tfl.broadcast_args"(%0, %1) : (tensor<8xi64>, tensor<4xi64>) -> tensor<8xi64>
  %3 = "tfl.shape"(%arg0) : (tensor<8x7x6x5x?x3x2x1xi1>) -> tensor<8xi64>

  // CHECK-LABEL:mul_with_high_dims_dynamic_shape_both_sides
  // CHECK: %[[SHAPE:.*]] = "tfl.shape"(%arg0) : (tensor<8x7x6x5x?x3x2x1xf32>) -> tensor<8xi64>
  // CHECK: %[[SHAPE_1:.*]] = "tfl.shape"(%arg1) : (tensor<?x3x2x1xf32>) -> tensor<4xi64>
  // CHECK: %[[BROADCAST_ARGS:.*]] = "tfl.broadcast_args"(%[[SHAPE]], %[[SHAPE_1]]) : (tensor<8xi64>, tensor<4xi64>) -> tensor<8xi64>

    %1 = "tf.XlaSharding"(%arg0) {_XlaSharding = "", sharding = "", unspecified_dims = []} : (tensor<i32>) -> tensor<i32>
    // expected-remark@above {{ID: 1}}
    %2:2 = "tf.XlaCallModule"(%1) {Sout = [#tf_type.shape<>, #tf_type.shape<>], dim_args_spec = [], function_list = [@__inference_callable_flat_tf_150], module = "ML\EFR\00__inference_callable_flat_tf_15\00", platforms = [], version = 5 : i64} : (tensor<i32>) -> (tensor<i32>, tensor<i32>)

}
// CHECK-LABEL: broadcast_in_dim
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2x!quant.uniform<i8:f32, 2.000000e+00:3>>
// CHECK: %[[SHAPE:.+]] = arith.constant
// CHECK{LITERAL}: dense<[3, 2]> : tensor<2xi32>
// CHECK: %[[BROADCAST:.+]] = "tfl.broadcast_to"(%[[ARG0]], %[[SHAPE]]) : (tensor<1x2x!quant.uniform<i8:f32, 2.000000e+00:3>>, tensor<2xi32>) -> tensor<3x2x!quant.uniform<i8:f32, 2.000000e+00:3>>
// CHECK: return %[[BROADCAST]]

      "engines": {
        "node": ">=12"
      }
    },
    "node_modules/d3-shape": {
      "version": "3.2.0",
      "resolved": "https://registry.npmjs.org/d3-shape/-/d3-shape-3.2.0.tgz",
      "integrity": "sha512-SaLBuwGm3MOViRq2ABk3eLoxwZELpH6zhl3FbAoJ7Vm1gofKx6El1Ib5z23NUEhF9AsGl7y+dzLe5Cw2AArGTA==",
      "dependencies": {

2897,"touches":3,"min_t":1228948796,"max_t":1236205616,"mean_t":1231544399},{"name":"shapes-circle-02-t-expected.checksum","kids":[],"cl_weight":0.0015634355156012897,"touches":3,"min_t":1228948796,"max_t":1236205616,"mean_t":1231544399},{"name":"filters-conv-01-f-expected.checksum","kids":[],"cl_weight":0.0015634355156012897,"touches":3,"min_t":1228948796,"max_t":1236205616,"mean_t":1231544399},{"name":"shapes-ellipse-01-t-expected.checksum","kids":[],"cl_weight":0.0015634355156012897,"touches"...

the room.  The cook threw a frying-pan after her as she went out,
but it just missed her.

  Alice caught the baby with some difficulty, as it was a queer-
shaped little creature, and held out its arms and legs in all
directions, `just like a star-fish,' thought Alice.  The poor
little thing was snorting like a steam-engine when she caught it,

the room.  The cook threw a frying-pan after her as she went out,
but it just missed her.

  Alice caught the baby with some difficulty, as it was a queer-
shaped little creature, and held out its arms and legs in all
directions, `just like a star-fish,' thought Alice.  The poor
little thing was snorting like a steam-engine when she caught it,

that Object PR will appear upon the Paper in its proper shape and Colours. For as the Light which comes from the Point Q goes to the Point _q_, so the Light which comes from other Points P and R of the Object, will go to so many other correspondent Points _p_ and _r_ (as is manifest by the sixth Axiom;) so that every Point of the Object shall illuminate a correspondent Point of the Picture, and thereby make a Picture like the Object in Shape and Colour, this only excepted, that the Picture shall be...

EOF
      if [[ "${master}" == "false" ]]; then
          # TODO(kubernetes/autoscaler#718): AUTOSCALER_ENV_VARS is a hotfix for cluster autoscaler,
          # which reads the kube-env to determine the shape of a node and was broken by #60020.
          # This should be removed as soon as a more reliable source of information is available!
          local node_labels
          local node_taints