"uniqueItems": true
            }
          },
          {
            "description": "resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details.\n\nDefaults to unset",
            "in": "query",

            "uniqueItems": true
          }
        },
        {
          "description": "resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details.\n\nDefaults to unset",
          "in": "query",

	// result: (Move {types.Types[types.TUINT8]} [int64(sz)] dst src mem)
	for {
		if auxIntToInt64(v.AuxInt) != 0 {
			break
		}
		call := v_0
		if call.Op != OpStaticCall || len(call.Args) != 4 {
			break
		}
		sym := auxToCall(call.Aux)
		mem := call.Args[3]
		dst := call.Args[0]
		src := call.Args[1]
		call_2 := call.Args[2]
		if call_2.Op != OpConst64 {

        requires all optimizer variables to be created at the first
        `apply_gradients()` or `minimize()` call. If your workflow calls
        optimizer to update different parts of model in multiple stages,
        please call `optimizer.build(model.trainable_variables)` before the
        training loop.
    *   **Performance regression on `ParameterServerStrategy`.** This could be

		if auxIntToInt64(v.AuxInt) != 0 {
			break
		}
		call := v_0
		if call.Op != OpARM64CALLstatic || len(call.Args) != 4 {
			break
		}
		sym := auxToCall(call.Aux)
		mem := call.Args[3]
		dst := call.Args[0]
		src := call.Args[1]
		call_2 := call.Args[2]
		if call_2.Op != OpARM64MOVDconst {
			break
		}
		sz := auxIntToInt64(call_2.AuxInt)

		{"(*Server).ServeRequest", Method, 0},
		{"(ServerError).Error", Method, 0},
		{"Accept", Func, 0},
		{"Call", Type, 0},
		{"Call.Args", Field, 0},
		{"Call.Done", Field, 0},
		{"Call.Error", Field, 0},
		{"Call.Reply", Field, 0},
		{"Call.ServiceMethod", Field, 0},
		{"Client", Type, 0},
		{"ClientCodec", Type, 0},
		{"DefaultDebugPath", Const, 0},

	for {
		if auxIntToInt64(v.AuxInt) != 0 {
			break
		}
		call := v_0
		if call.Op != OpAMD64CALLstatic || len(call.Args) != 4 {
			break
		}
		sym := auxToCall(call.Aux)
		mem := call.Args[3]
		dst := call.Args[0]
		src := call.Args[1]
		call_2 := call.Args[2]
		if call_2.Op != OpAMD64MOVQconst {
			break
		}
		sz := auxIntToInt64(call_2.AuxInt)

Angles of Incidence, Reflexion, and Refraction._


DEFIN. VII

_The Light whose Rays are all alike Refrangible, I call Simple,
Homogeneal and Similar; and that whose Rays are some more Refrangible
than others, I call Compound, Heterogeneal and Dissimilar._ The former
Light I call Homogeneal, not because I would affirm it so in all
respects, but because the Rays which agree in Refrangibility, agree at

                          type: integer
                        perTryTimeout:
                          description: Timeout per attempt for a given request, including
                            the initial call and any retries.
                          type: string
                          x-kubernetes-validations:
                          - message: must be a valid duration greater than 1ms

TensorArray gradient calls use an accumulator TensorArray object.  If
multiple gradients are calculated and run in the same session, the multiple
gradient nodes may accidentally flow through the same accumulator TensorArray.
This double counts and generally breaks the TensorArray gradient flow.

The solution is to identify which gradient call this particular