out1[21] = x45
	out1[22] = x47
	out1[23] = x48
	out1[24] = x49
	out1[25] = x51
	out1[26] = x53
	out1[27] = x55
	out1[28] = x57
	out1[29] = x59
	out1[30] = x61
	out1[31] = x62
	out1[32] = x63
	out1[33] = x65
	out1[34] = x67
	out1[35] = x69
	out1[36] = x71
	out1[37] = x73
	out1[38] = x75
	out1[39] = x76
	out1[40] = x77
	out1[41] = x79
	out1[42] = x81

    arg: the tensor to be set in the resource array.
    index: the index in the resource array
  }];

  let arguments = (ins
    TFTensorType:$arg,
    I64Attr:$index
  );

  let results = (outs);

  let assemblyFormat = "operands attr-dict";
}

def SetResourceDhtOp : FallbackSync_Op<"set_resource_dht", [CoreRT_TypedAttributeTrait]> {
  let summary = "Set a DHT in resource array";

  let description = [{

  }];

  let arguments = (ins);

  let results = (outs
    Res<TF_Int32Tensor, [{A vector containing the global TPU id of each TPU on the host.}]>:$output
  );
}

def TF_ShutdownTPUSystemOp : TF_Op<"ShutdownTPUSystem", []> {
  let summary = [{
An op that shuts down the TPU system.
  }];

  let arguments = (ins);
  let results = (outs
    TF_BoolTensor:$success
  );
}

  );

  let results = (outs);
}


def TFL_HashtableSizeOp: TFL_Op<"hashtable_size", []> {
  let summary = "Computes the number of elements in the given table.";

  let arguments = (ins
    TFL_ResourceTensor:$hash_table
  );

  let results = (outs
    TFL_I64Tensor:$out
  );
}

def TFL_BroadcastArgsOp : TFL_Op<"broadcast_args",[

            continue entries;
          }
        }
        if (outI < i) { // if outI == i don't bother writing the values back where they came from
          alternatingKeysAndValues[outKeyIndex] = key;
          alternatingKeysAndValues[outKeyIndex ^ 1] = value;
        }
        outI++;
      }
      return outI == n ? hashTable : new Object[] {hashTable, outI, duplicateKey};
    } else if (tableSize <= SHORT_MAX_SIZE) {
      /*

    in_ch and out_ch are for control dependencies.
  }];

  let arguments = (ins
    TFRT_ChainType:$in_ch,
    TFTensorType:$arg,
    StrAttr:$device,
    I64Attr:$index
  );

  let results = (outs
    TFRT_ChainType:$out_ch
  );

  let assemblyFormat = "operands attr-dict";
}

def GetResourceOp : FallbackAsync_Op<"get_resource",
    [CoreRT_TypedAttributeTrait,

	out1[21] = x48
	out1[22] = x50
	out1[23] = x51
	out1[24] = x52
	out1[25] = x54
	out1[26] = x56
	out1[27] = x58
	out1[28] = x60
	out1[29] = x62
	out1[30] = x64
	out1[31] = x65
	out1[32] = x66
	out1[33] = x68
	out1[34] = x70
	out1[35] = x72
	out1[36] = x74
	out1[37] = x76
	out1[38] = x78
	out1[39] = x79
	out1[40] = x80
	out1[41] = x82
	out1[42] = x84

	if _, err := outf.Seek(int64(dwarfCmdOffset), 0); err != nil {
		return err
	}
	if _, err := io.CopyN(outf, bytes.NewReader(realdwarf.Raw()), int64(realdwarf.Len)); err != nil {
		return err
	}
	if _, err := outf.Seek(int64(unsafe.Offsetof(exem.FileHeader.Ncmd)), 0); err != nil {
		return err
	}
	if err := binary.Write(outf, exem.ByteOrder, exem.Ncmd+1); err != nil {
		return err
	}

  );

  let results = (outs
    TF_NumberTensor:$out
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
  TF_DerivedOperandTypeAttr Tidx = TF_DerivedOperandTypeAttr<1>;

  let hasVerifier = 1;
}

def TF_CumsumOp : TF_Op<"Cumsum", [Pure, TF_AllTypesMatch<["x", "out"]>]> {

// QDQ-NEXT: %[[out1:.*]] = "tfl.dequantize"(%[[split]]#0) : (tensor<2x!quant.uniform<u8:f32, 1.000000e+00>>) -> tensor<2xf32>
// QDQ-NEXT: %[[out2:.*]] = "tfl.dequantize"(%[[split]]#1) : (tensor<2x!quant.uniform<u8:f32, 1.000000e+00>>) -> tensor<2xf32>
// QDQ-NEXT: return %[[out1]], %[[out2]] : tensor<2xf32>, tensor<2xf32>
}

// CHECK-LABEL: RemoveTrival