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());

  let summary = [{
    Simulates the effect of uniform quantization with const range.
  }];

  let description = [{
    Given a const min, max, num_bits and narrow_range attribute, applies the
    same uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_args op. See the fakeQuantAttrsToType() utility

  let summary = [{
    Simulates the effect of uniform quantization with const range.
  }];

  let description = [{
    Given a const min, max, num_bits and narrow_range attribute, applies the
    same uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_args op. See the fakeQuantAttrsToType() utility

				s.Label = labels
			}
			if len(numLabels) > 0 {
				s.NumLabel = numLabels
				for key, units := range numUnits {
					if len(units) > 0 {
						numUnits[key] = padStringArray(units, len(numLabels[key]))
					}
				}
				s.NumUnit = numUnits
			}
		}

		s.Location = locBuffer[:len(s.locationIDX)]
		locBuffer = locBuffer[len(s.locationIDX):]
		for i, lid := range s.locationIDX {

    """Quantizes and dequantizes hist_mids using quant_min and quant_max.

    Quantization converts the range of numbers from [quant_min, quant_max] to
    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
    values greater than quant_max are converted to 2^num_bits - 1.

    The histogram represents the distribution of the data, and our goal is to
    find the quant_min and quant_max that best describe this distribution. To do

  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");

    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)

namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         tensorflow::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;

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(); }