weight = array_ops.fake_quant_with_min_max_args(
        weight, min=-0.1, max=0.2, num_bits=8, narrow_range=False
    )

    # shape: (2, 2)
    output_tensor = math_ops.matmul(matmul_input, weight)
    # Insert fake quant to simulate a QAT model.
    output_tensor = array_ops.fake_quant_with_min_max_args(
        output_tensor, min=-0.2, max=0.2, num_bits=8, narrow_range=False
    )

			}
			tagMismatch := !isReplicated(tagCount, numSites, tagSet)
			olockCfgMismatch := !isReplicated(olockCfgCount, numSites, olockConfigSet)
			sseCfgMismatch := !isReplicated(sseCfgCount, numSites, sseCfgSet)
			versionCfgMismatch := !isReplicated(versionCfgCount, numSites, versionCfgSet)
			policyMismatch := !isBktPolicyReplicated(numSites, policies)
			replCfgMismatch := !isBktReplCfgReplicated(numSites, replCfgs)

void TF_SetShape(TF_Tensor* t, const int64_t* dims, int num_dims) {
  tensorflow::down_cast<tensorflow::TensorInterface*>(t->tensor)->SetShape(
      dims, num_dims);
}

int TF_NumDims(const TF_Tensor* t) { return t->tensor->NumDims(); }

int64_t TF_Dim(const TF_Tensor* t, int dim_index) {
  return t->tensor->Dim(dim_index);
}

size_t TF_TensorByteSize(const TF_Tensor* t) { return t->tensor->ByteSize(); }

    Type weight_type;
    if (IsPerTensor(weight_only_ptq)) {
      weight_type = dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedTypeForWeight(
              attr, /*symmetric=*/true, /*num_bits=*/8, /*is_signed=*/true,
              /*narrow_range=*/true, /*legacy_float_scale=*/false));
    } else {
      int quantization_dimension = GetQuantizationDimension(

  input: "BoxPredictor_4/ClassPredictor/weights_quant/max"
  attr {
    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"

  input: "BoxPredictor_4/ClassPredictor/weights_quant/max"
  attr {
    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 4
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"

  input: "BoxPredictor_4/ClassPredictor/weights_quant/max/read"
  attr {
    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 4
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"

  input: "BoxPredictor_4/ClassPredictor/weights_quant/max/read"
  attr {
    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"

  if (!IsLegalQuantSpecs(func)) {
    return true;
  }

  OpBuilder builder(func);
  bool is_signed = quant_specs_.IsSignedInferenceType();
  IntegerAttr num_bits =
      builder.getI32IntegerAttr(quant_specs_.GetQuantizationTypeWidth());
  BoolAttr narrow_range = builder.getBoolAttr(false);

  auto add_quantize_op = [&](Location loc, Type input_type, Block* block,

	format.Erasure.Version = formatErasureVersionV3
	format.Erasure.DistributionAlgo = formatErasureVersionV3DistributionAlgoV3
	format.Erasure.Sets = make([][]string, numSets)

	for i := 0; i < numSets; i++ {
		format.Erasure.Sets[i] = make([]string, setLen)
		for j := 0; j < setLen; j++ {
			format.Erasure.Sets[i][j] = mustGetUUID()
		}
	}
	return format
}