%0 = "quantfork.stats"(%arg0) {layerStats = dense<[6.00000000e-6, 9.00000000e-1]> : tensor<2xf32>} : (tensor<1x2xf32>) -> tensor<1x2xf32>

        // static shape for `reshape`.  This is a problem because side-effecting
        // ops like RandomUniformInt() cannot be constant folded.  We fix this
        // by putting `shape` and `reshape` in different clusters, which results
        // in us recompiling `reshape`'s cluster for every new value of `shape`,
        // making `reshape` statically sized within each compilation.  We

  // CHECK: %[[RESULT:.*]] = "tf.StridedSlice"(%arg0, %[[CST]], %[[CST0]], %[[CST1]])
  // CHECK-SAME: begin_mask = 7 : i64
  // CHECK-SAME: ellipsis_mask = 0 : i64
  // CHECK-SAME: end_mask = 14 : i64
  // CHECK-SAME: new_axis_mask = 0 : i64
  // CHECK-SAME: shrink_axis_mask = 0 : i64

              "divisible by num_cores_per_replica="
           << num_cores_per_replica;
  }
  llvm::SmallVector<int64_t, 4> shape;
  shape.push_back(split_size);
  for (int i = 1; i < in_shape.size(); ++i) {
    shape.push_back(in_shape[i]);
  }
  shard_type = RankedTensorType::Builder(ranked_type).setShape(shape);
  return mlir::success();
}

// Output `sharding`, which is the sharding of `val`. `context_op` is used for

          rewriter.getI32IntegerAttr(ConvertToTfliteSize(size));
    }
    auto shape_attr = DenseElementsAttr::get(shape_type, result_shape_data);
    auto shape = rewriter.create<TF::ConstOp>(loc, shape_type, shape_attr);

    return rewriter.create<TF::ReshapeOp>(loc, result_type, filter, shape);
  }
};

// StridedSlice can have complicated attributes like begin_axis_mask,

  """
  TENSOR = 1
  TENSOR_LIST = 2
  ATTR = 3
  NONE = 4
  SHAPE = 5  # shape -> !shape.shape
  I1 = 21
  I8 = 22
  I16 = 23
  I32 = 24
  I64 = 25
  F32 = 26
  INDEX = 27
  AG_UNDEFINED_VAL = 100
  AG_BUILTIN_FUNC = 101
  TF_RAW_OP = 102
  TF_REGION = 103
  TF_TENSOR_SHAPE_FUNC = 104  # shape.as_list
  TF_TENSOR_SHAPE_LIST = 105  # shape.as_list()
  PY_BUILTIN_FUNC = 200
  TFR_BUILTIN_FUNC = 201

}

TEST(CAPI, ShapeInferenceError) {
  // TF_FinishOperation should fail if the shape of the added operation cannot
  // be inferred.
  TF_Status* status = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Create this failure by trying to add two nodes with incompatible shapes
  // (A tensor with shape [2] and a tensor with shape [3] cannot be added).
  const char data[] = {1, 2, 3};
  const int64_t vec2_dims[] = {2};

  const float eps = 1e-7;
  EXPECT_THAT(expected_tensor, NotNull());
  EXPECT_THAT(tensor->is_variable, Eq(expected_tensor->is_variable));
  EXPECT_THAT(tensor->shape, Eq(expected_tensor->shape));
  EXPECT_THAT(tensor->type, Eq(expected_tensor->type));
  const auto quantization_params = tensor->quantization.get();
  const auto expected_quantization_params = expected_tensor->quantization.get();

    %cst_0 = arith.constant dense<0> : tensor<i32>
    %cst_1 = arith.constant dense<-1> : tensor<1xi32>
    %cst_2 = arith.constant dense<1> : tensor<1xi32>
    %cst_3 = arith.constant dense<0> : tensor<1xi32>
    %0 = "tfl.shape"(%arg2) {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<?x?xi32>) -> tensor<2xi32>

      mlir::SmallVector<mlir::Attribute, 4> shape;
      for (auto s : new_shape) {
        shape.push_back(
            builder.getI32IntegerAttr(mlir::TFL::ConvertToTfliteSize(s)));
      }
      auto output_shape = DenseElementsAttr::get(shape_type, shape);
      auto shape_op = builder.create<tfl::ConstOp>(loc, output_shape);
      op_state.addOperands({shape_op});