runnext := atomic.Loaduintptr((*uintptr)(unsafe.Pointer(&pp.runnext)))
		if tail == atomic.Load(&pp.runqtail) {
			return head == tail && runnext == 0
		}
	}
}

// To shake out latent assumptions about scheduling order,
// we introduce some randomness into scheduling decisions
// when running with the race detector.
// The need for this was made obvious by changing the

  }

  SmallVector<int64_t> shape;
  bool refined_shape = false;
  // Build the shape of the refined type, if lhs is unranked it
  // will be directly the shape of the refined type, otherwise we merged by
  // taking the most specialized. This combines `10x?x?` and `?x?x8` into
  // `10x?x8`.
  if (!lhs_shape_type.hasRank()) {
    if (rhs_shape_type.hasRank()) {
      shape.append(rhs_shape_type.getShape().begin(),

    # Checks that each variable's name and shape match.
    self.assertEqual(len(variable_node_defs), len(var_name_and_shapes))
    for var_name, shape in var_name_and_shapes:
      self.assertIn(var_name, variable_node_defs)
      self.assertEqual(
          shape,
          tensor_shape.TensorShape(
              variable_node_defs[var_name].attr['shape'].shape
          ),
      )

  let summary =
    "Replicate TensorList init ops for correct shape assignments in shape inference";

  let description = [{
    If we pass same TensorList to a while op as multiple arguments or just use
    the same TensorList at multiple places and assign different
    TensorListSetItem to elements of TensorList, the shape inference is then
    unable to identify the Shape of these args and thus the input TensorList

element. For example, if you have a single image of shape `[height, width,
channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
which will make the shape `[1, height, width, channels]`.

Other examples:

```
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]

      GetTFLiteType(tensor_type.getElementType()).value();
  std::vector<int32_t> shape;
  if (tensor_type.hasRank()) {
    llvm::ArrayRef<int64_t> shape_ref = tensor_type.getShape();
    shape = std::vector<int32_t>(shape_ref.begin(), shape_ref.end());
  }

  variant_params.push_back(
      tflite::CreateVariantSubType(builder_, builder_.CreateVector(shape),
                                   tflite_element_type, tensor_type.hasRank()));

  // 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>

  %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: 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]]

	case 4 << 10:
		return &bufioWriter4kPool
	}
	return nil
}

// newBufioReader should be an internal detail,
// but widely used packages access it using linkname.
// Notable members of the hall of shame include:
//   - github.com/gobwas/ws
//
// Do not remove or change the type signature.
// See go.dev/issue/67401.
//
//go:linkname newBufioReader
func newBufioReader(r io.Reader) *bufio.Reader {