"schema": {
              "type": "boolean",
              "uniqueItems": true
            }
          },
          {
            "description": "Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity.",
            "in": "query",
            "name": "timeoutSeconds",
            "schema": {
              "type": "integer",

          "schema": {
            "type": "boolean",
            "uniqueItems": true
          }
        },
        {
          "description": "Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity.",
          "in": "query",
          "name": "timeoutSeconds",
          "schema": {
            "type": "integer",
            "uniqueItems": true

	// 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)

                          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

  ```

If more than one session.run call is simultaneously trying to compute `b`
the values of `a` will be gathered, non-deterministically concatenated
along the first axis, and only one thread will run the computation.

Assumes that all arguments of the function are Tensors which will be batched
along their first dimension.

Arguments that are captured, are not batched. The session.run call which does

                          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