%n: i32 {tfr.name="N"},
                      %axis: i32 {tfr.name="axis"}) -> !tfr.tensor {
  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i32
  %eq = arith.cmpi eq, %n, %cst : i32
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %temp = tfr.call @tf__expand_dims(%v1, %axis) : (!tfr.tensor, i32) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {

rRedrawOverlay();if(!preventEvent)plot.getPlaceholder().trigger("plotunselected",[])}}function extractRange(ranges,coord){var axis,from,to,key,axes=plot.getAxes();for(var k in axes){axis=axes[k];if(axis.direction==coord){key=coord+axis.n+"axis";if(!ranges[key]&&axis.n==1)key=coord+"axis";if(ranges[key]){from=ranges[key].from;to=ranges[key].to;break}}}if(!ranges[key]){axis=coord=="x"?plot.getXAxes()[0]:plot.getYAxes()[0];from=ranges[coord+"1"];to=ranges[coord+"2"]}if(from!=null&&to!=null&&from>to){var...

          size_splits=[left_padding_size, -1],
          axis=i,
          num_split=2)
      _, right_padding = tf.raw_ops.SplitV(
          value=rarray,
          size_splits=[-1, right_padding_size],
          axis=i,
          num_split=2)
    else:
      _, left_padding = tf.raw_ops.SplitV(
          value=rarray,
          size_splits=[-1, left_padding_size],
          axis=i,
          num_split=2)

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

message QuantizationInfo {
  // min/max of the per axis value range. To quantize the value, the metadata
  // of the target properties should be specified or read from the ops
  // quantization specification.
  message MinMax {
    float min = 1;
    float max = 2;
  }

  // Affine parameters to quantize the per axis value. The metadata of the
  // target properties should be specified as well.

  %0 = "tfl.dequantize"(%arg0) : (tensor<1000x1000x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000x1000xf32>
  %1 = "tfl.gather"(%0, %arg1) {axis = 0 : i32, batch_dims = 0 : i32}: (tensor<1000x1000xf32>, tensor<1x1xi32>) -> tensor<1x1x1000xf32>
  func.return %1 : tensor<1x1x1000xf32>

// CHECK-NEXT: tfl.gather
// CHECK-NEXT: tfl.dequantize
}

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

}

inline bool IsUnknownDimOrRank(int64_t dim_or_rank) {
  return dim_or_rank == -1;
}

// Returns dimension index for the given TensorFlow axis that supports negative
// indexing.
inline int64_t GetDimForAxis(int64_t axis, int64_t rank) {
  return axis >= 0 ? axis : axis + rank;
}

// Returns the tf.Equal/tf.NotEqual result type given `x` and `y` and inputs. If

module {
func.func @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
  %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<i64>) -> tensor<6x3xf32>
  func.return %1 : tensor<6x3xf32>
}

                $.each(plot.getXAxes(), function(_, axis) {
                    const opts = axis.options;
                    opts.min = reset ? null : ranges.xaxis.from;
                    opts.max = reset ? null : ranges.xaxis.to;
                });
                $.each(plot.getYAxes(), function(_, axis) {
                    const opts = axis.options;
                    opts.min = reset ? 0 : ranges.yaxis.from;