(getElementTypeOrSelf(op.getOutput().getType()))))
      return failure();

    ElementsAttr input_tensor = qconst_op.getValue();

    assert(perm_tensor.getType().getRank() == 1);
    const int num_dimensions = input_tensor.getShapedType().getRank();
    assert(perm_tensor.getType().getNumElements() == num_dimensions);

    ArrayRef<int64_t> input_shape = input_tensor.getShapedType().getShape();

      return failure();
    }

    int quant_dim = -1;
    if (PerAxis) {
      // This is a special case that the quant_dim is the last dimensions.
      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());

        tf_op.emitError("The input should have known rank for per-channel op.");
        return failure();
      }
      // This is a special case that the quant_dim is the last dimensions.
      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());

    if (PerAxis) {
      // This is a special case that the quant_dim is the last dimensions
      // according to the tf.FakeQuantWithMinMaxPerChannel.
      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());

          func, arg_number, symbol_table);
      if (!global_tensor) continue;

      auto arg_type = mlir::cast<RankedTensorType>(arg.getType());
      assert(arg_type.getRank() == 0);
      llvm::ArrayRef<TensorType> underlying_type =
          mlir::cast<TF::ResourceType>(arg_type.getElementType()).getSubtypes();

      // If the arg type already matches the global_tensor type, we don't need

    if (std::isalpha(label)) {
      new_lhs.push_back(label);
    } else {
      // Encounter ellipsis: generate unnamed labels then insert to the new
      // labels.
      new_labels = GenerateLabels(lhs_ty.getRank() - lhs_named_label_count,
                                  available_labels);
      new_lhs.append(new_labels);
      i += 2;
    }
  }

  std::string new_rhs, new_rhs_labels;

// Checks if the value has rank of `n`.
class HasRankOf<int n> : Constraint<
  CPred<"$0.getType().cast<ShapedType>().hasRank() && "
        "$0.getType().cast<ShapedType>().getRank() == " # n>,
  "Checks if the value has rank of 'n'.">;

// Checks if the value has static shape.
def HasStaticShapeConstraint : Constraint<CPred<"HasStaticShape($0)">>;

                  .getBroadcastDimensions()
            : nullptr;
    if (broadcast_dims == nullptr) {
      const auto filter_rank = filter_value.getShapedType().getRank();
      auto dimsType = RankedTensorType::get({1}, rewriter.getIntegerType(64));
      broadcast_dims = DenseIntElementsAttr::get(dimsType, {filter_rank - 1});
    }

  auto boxes_type =
      mlir::dyn_cast<RankedTensorType>(func_.getFunctionType().getInput(0));
  if (boxes_type == nullptr || !boxes_type.hasRank() ||
      boxes_type.getRank() != 2) {
    return func_.emitWarning() << "TFLite does not support batched input for "
                                  "non_max_suppression_padded";
  }
  return success();
}

    // and {num_channels} (rank 1) for per-channel quantized one.
    auto scale_type = filter_scale_attr.getType().dyn_cast<RankedTensorType>();
    if (scale_type.getRank() != 0 && scale_type.getRank() != 1) {
      return failure();
    }
    SmallVector<float> scale_factors;
    scale_factors.reserve(filter_scale_attr.size());
    for (auto value : filter_scale_attr.getValues<APFloat>()) {