* Support trust id as a scope in the OpenStack authentication logic ([#32111](https://github.com/kubernetes/kubernetes/pull/32111), [@MatMaul](https://github.com/MatMaul))
* Only wait for cache syncs once in NodeController ([#34851](https://github.com/kubernetes/kubernetes/pull/34851), [@ncdc](https://github.com/ncdc))

  // - rhs: [RHSBATCHDIMS..., RHSROWS, RHSCOLS]
  // - result: [broadcast(LHSBATCHDIMS, RHSBATCHDIMS)..., LHSROWS, RHSCOLS]
  // To perform the matmul, we need to first broadcast lhs and rhs to a common
  // set of leading dimensions before doing the actual matmul.
  // That's what the code below does.
  // In particular, we populate out_lhs and out_rhs to have dimension structure:

* `tf.config.experimental.enable_tensor_float_32_execution`

      type: DT_INT32
    }
  }
  attr {
    key: "keep_dims"
    value {
      b: false
    }
  }
}
node {
  name: "fc1000_1/MatMul"
  op: "MatMul"
  input: "avg_pool_1/Mean"
  input: "fc1000_1/MatMul/ReadVariableOp"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }

}

def TF__FusedMatMulOp : TF_Op<"_FusedMatMul", [Pure, TF_SameOperandsAndResultElementTypeResolveRef]> {
  let summary = [{
Performs a MatMul followed by a specified series of operations.
  }];

  let description = [{
The inputs to the MatMul are specified by `a` and `b`. The series of operations
that follows is specified by the `fused_ops` attribute, which is a list of TF op

      <match value="#!/usr/bin/env wolframscript" type="string" offset="0"/>
    </magic>
    <sub-class-of type="application/mathematica"/>
  </mime-type>

  <mime-type type="application/mathml+xml">
    <glob pattern="*.mathml"/>
  </mime-type>
  <mime-type type="application/mbms-associated-procedure-description+xml"/>
  <mime-type type="application/mbms-deregister+xml"/>
  <mime-type type="application/mbms-envelope+xml"/>