%4 = "tf.Add"(%2, %arg2) : (tensor<?x8x10xf32>, tensor<8x10xf32>) -> tensor<?x8x10xf32>
  %5 = "tf.Add"(%arg1, %arg2) : (tensor<8x10xf32>, tensor<8x10xf32>) -> tensor<8x10xf32>
  %6 = "tf.Const"() {_output_shapes = ["tfshape$"], device = "/device:CPU:0", dtype = f32, value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>

                    )
                ]
            )
        )
        self.assertTrue(
            self._contains_op(
                output_graphdef,
                'Const',
                '_output_shapes',
                per_channel_size_attr,
            )
        )
    elif target_opset == quant_opts_pb2.UNIFORM_QUANTIZED:
      self.assertTrue(

  if (node.IsWhileNode()) {
    auto* output_shapes = node.attrs().Find("output_shapes");
    auto* element_types = node.attrs().Find("T");
    if (output_shapes && !output_shapes->list().shape().empty()) {
      const auto& output_shape = output_shapes->list().shape(idx);
      const auto& element_type = element_types->list().type(idx);
      return ConvertToMlirTensorType(output_shape, element_type, &builder);
    }
  }

  int64_t out_row_dim = output_shape[output_shape.size() - 2];
  int64_t out_col_dim = output_shape[output_shape.size() - 1];

  int64_t expected_out_row_dim = op.getAdjX() ? x_col_dim : x_row_dim;
  int64_t expected_out_col_dim = op.getAdjY() ? y_row_dim : y_col_dim;

  if (expected_out_row_dim != ShapedType::kDynamic &&
      out_row_dim != ShapedType::kDynamic &&
      out_row_dim != expected_out_row_dim)

  }

  auto output_shape = xla::ShapeInference::InferGatherShape(
      input_shape, start_indices_shape, gather_dim_numbers, slice_sizes);
  if (!output_shape.ok()) {
    op->emitError() << output_shape.status().message();
    return false;
  }

  auto refined_type = xla::ConvertShapeToType<RankedTensorType>(
      *output_shape, mlir::Builder(op));
  if (!refined_type.ok()) {

  int64_t out_row_dim = output_shape[output_shape.size() - 2];
  int64_t out_col_dim = output_shape[output_shape.size() - 1];

  int64_t expected_out_row_dim = op.getAdjX() ? x_col_dim : x_row_dim;
  int64_t expected_out_col_dim = op.getAdjY() ? y_row_dim : y_col_dim;

  if (expected_out_row_dim != ShapedType::kDynamic &&
      out_row_dim != ShapedType::kDynamic &&
      out_row_dim != expected_out_row_dim)

    %0 = "tf.TakeDataset"(%arg0, %cst) {device = "", metadata = "", output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string]} : (tensor<!tf_type.variant>, tensor<i64>) -> tensor<!tf_type.variant>

      }
      auto output_shape =
          mlir::cast<RankedTensorType>(conv_op.getResult().getType())
              .getShape();
      SmallVector<int64_t, 4> transposed_output_shape = {
          output_shape[dnums.getOutputBatchDimension()],
          output_shape[dnums.getOutputSpatialDimensions().data()[0]],
          output_shape[dnums.getOutputSpatialDimensions().data()[1]],

      mlir::cast<ShapedType>(input.getType()).getShape();
  ArrayRef<int64_t> output_shape =
      mlir::cast<ShapedType>(output.getType()).getShape();

  int64_t agg_value = 1;
  for (size_t i = agg_start_idx; i < input_shape.size() - 1; ++i) {
    agg_value *= input_shape[i];
  }

  return (agg_value == output_shape[agg_start_idx]);
}

// Returns whether the given type `a` is broadcast-compatible with `b`.

        // expected-remark@above {{ID: 0}}