RuntimeError: x86 is not a valid value for quantized engine

Trying to quantize my model using this documentation Backend/Hardware Support and get this error - “RuntimeError: x86 is not a valid value for quantized engine”.

I followed this instruction:

# set the qconfig for PTQ
# Note: the old 'fbgemm' is still available but 'x86' is the recommended default on x86 CPUs
qconfig ='x86')
# or, set the qconfig for QAT
qconfig ='x86')
# set the qengine to control weight packing
torch.backends.quantized.engine = 'x86'

Using this model:

from torch.quantization import QuantStub, DeQuantStub

class QuantizedSiameseNetwork(nn.Module):
        Siamese network for image similarity estimation.
        The network is composed of two identical networks, one for each input.
    def __init__(self, embedding_size=2, use_quant=False):
        self.backbone = Backbone(embedding_size)
        self.use_quant = use_quant
        if self.use_quant:
            self.quant_m = QuantStub()
            self.dequant_m = DeQuantStub()

And this code is how I tried to implement it:

model_fp32 = QuantizedSiameseNetwork(2, True).to('cpu')
backend = "x86"
model_fp32.qconfig = torch.quantization.get_default_qconfig(backend)
torch.backends.quantized.engine = backend

model_static_quantized = torch.quantization.prepare(model_fp32, inplace=False)

I find the answer. In my version of PyCharm I should use fbgemm instead of x86