quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

      quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

      quant_dim = input_type.getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

  min = fakequant_op.getODSOperands(1);
  max = fakequant_op.getODSOperands(2);
  {
    auto target_attr = op->getAttrOfType<IntegerAttr>("num_bits");
    if (!target_attr)
      target_attr = rewriter.getIntegerAttr(rewriter.getIntegerType(64), 8);
    num_bits = target_attr;
  }
  {
    auto target_attr = op->getAttrOfType<BoolAttr>("narrow_range");

			}
			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"