}

// CHECK-LABEL: testGatherToV2
// Ensures that axis param and batch_dims attr use their default values of 0.
func.func @testGatherToV2(%params: tensor<4x3xf32>, %indices: tensor<1x2xi32>) -> tensor<2x3xf32> {
  // CHECK: %[[AXIS:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

  %30 = "tf.GatherV2"(%9, %29, %5) {batch_dims = 0 : i64, device = ""} : (tensor<32x1xf32>, tensor<2x16xi32>, tensor<i32>) -> tensor<2x16x1xf32>
  %31 = "tf.AddV2"(%19, %28) {device = ""} : (tensor<2x16xi32>, tensor<2x16xi32>) -> tensor<2x16xi32>
  %32 = "tf.GatherV2"(%9, %31, %5) {batch_dims = 0 : i64, device = ""} : (tensor<32x1xf32>, tensor<2x16xi32>, tensor<i32>) -> tensor<2x16x1xf32>

  // CHECK: [[VAL4:%.+]] = "tf.Reshape"(%arg0, [[VAL2]])
  // CHECK: [[VAL5:%.+]] = "tf.GatherV2"([[VAL4]], [[VAL3]], [[VAL0]]) <{batch_dims = 0 : i64}>
  // CHECK: [[VAL6:%.+]] = "tf.Reshape"([[VAL5]], [[VAL1]])
  // CHECK: return [[VAL6]]

  MEAN = 1,
  SQRTN = 2,
}

table EmbeddingLookupSparseOptions {
  combiner:CombinerType;
}

table GatherOptions {
  axis: int;
  // Parameters for Gather version 5 or above.
  batch_dims: int = 0;
}

table TransposeOptions {
}

table ExpOptions {
}

table CosOptions {
}

table ReducerOptions {
  keep_dims: bool;
}

table SqueezeOptions {

  //      CHECK: mhlo.torch_index_select
  // CHECK-SAME:   %[[PARAMS]], %[[INDICES]]
  // CHECK-SAME:   batch_dims = 1
  // CHECK-SAME:   dim = 2
  %axis = "tf.Const"() { value = dense<[-1]> : tensor<1xi32> } : () -> tensor<1xi32>
  %1 = "tf.GatherV2"(%params, %indices, %axis) {batch_dims = -1 : i64} : (tensor<16x2x3xf32>, tensor<16x5xi32>, tensor<1xi32>) -> tensor<16x2x5xf32>
  func.return %1 : tensor<16x2x5xf32>
}

  MEAN = 1,
  SQRTN = 2,
}

table EmbeddingLookupSparseOptions {
  combiner:CombinerType;
}

table GatherOptions {
  axis: int;
  // Parameters for Gather version 5 or above.
  batch_dims: int = 0;
}

table TransposeOptions {
}

table ExpOptions {
}

table CosOptions {
}

table ReducerOptions {
  keep_dims: bool;
}

    }
  }

  const int num_contract_dim = opponent_shape.size();
  opponent_required_dim -= used_rank;

  // Add redundant 1s to match the shape.
  // Required 1s = out_dims - # my batch_dims - my remaining dims.
  if (!is_lhs) {
    absl::c_reverse(opponent_shape);
  }
  for (int i = 0; i < opponent_required_dim; i++) {
    opponent_shape.push_back(1);
  }
  if (!is_lhs) {

func.func @QuantizeGatherWeightOnly(%arg0: tensor<3xi32>) -> tensor<3x3x3x3xf32> {
  %w = arith.constant dense<1.270000e+02> : tensor<64x3x3x3xf32>
  %emb = "tfl.gather"(%w, %arg0) {axis = 0 : i32, batch_dims = 0 : i32} : (tensor<64x3x3x3xf32>, tensor<3xi32>) -> tensor<3x3x3x3xf32>
  %emb_s = "quantfork.stats"(%emb) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<3x3x3x3xf32>) -> tensor<3x3x3x3xf32>

    TFL_TensorOf<[I16, I32, I64]>:$indices,
    I32Attr:$axis,
    DefaultValuedOptionalAttr<I32Attr, "0">:$batch_dims
  );

  let builders =
  [
    OpBuilder<(ins "Value":$params, "Value":$indices, "IntegerAttr":$axis, "IntegerAttr":$batch_dims),
    [{ BuildGatherOp(&$_builder, $_state, params, indices, axis, batch_dims); }]>
  ];

  let results = (outs

  auto _axis = _o->axis;
  auto _batch_dims = _o->batch_dims;
  return tflite::CreateGatherOptions(
      _fbb,
      _axis,
      _batch_dims);
}

inline TransposeOptionsT *TransposeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {