// CHECK: %[[RES:.*]] = "tf.Const"()
  // CHECK: return %[[RES]]
  %0 = "tf.Const"() {value = dense<4.200000e+01> : tensor<f32>} : () -> tensor<f32>
  %1 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<f32>>>
  "tf.AssignVariableOp"(%1, %0) : (tensor<!tf_type.resource<tensor<f32>>>, tensor<f32>) -> ()
  func.return
}

// -----

func.func @testUnimplementedOp() -> (tensor<i32>, tensor<i32>) {
  %0 = arith.constant dense<1> : tensor<i32>
  %1 = arith.constant dense<2> : tensor<i32>
  %2 = "tf.Maximum"(%0, %1) {_output_shapes = ["tfshape$"]} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %3 = "tf.Minimum"(%0, %1) {random_attr = "hello"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %2, %3: tensor<i32>, tensor<i32>

#include "tensorflow/c/experimental/saved_model/public/signature_def_param_list.h"
#include "tensorflow/c/experimental/saved_model/public/tensor_spec.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_shape.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/stringpiece.h"

  for (const std::optional<llvm::ArrayRef<int64_t>>& shape : shapes) {
    TensorShapeProto& tshape = *shape_list.add_shape();
    if (shape.has_value()) {
      for (int64_t dim : *shape) {
        tshape.add_dim()->set_size(mlir::ShapedType::isDynamic(dim) ? -1 : dim);
      }
    } else {
      tshape.set_unknown_rank(true);
    }
  }

  // If shape is already set, override it. This can happen if we import

        "@local_xla//xla/tsl/c:tsl_status_internal_headers",
    ],
    visibility = [
        "//tensorflow/python:__subpackages__",
    ],
)

cc_library(
    name = "tf_shape",
    srcs = ["tf_shape.cc"],
    hdrs = ["tf_shape.h"],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros",
        ":tf_shape_internal",
        "//tensorflow/core:framework",

  TFE_Op* reshape_op = TFE_NewOp(tfe_context_, "Reshape", status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  TFE_OpSetAttrType(reshape_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(reshape_op, "Tshape", TF_INT32);
  CheckOutputShapes(reshape_op,
                    /* input_shapes*/ {unknown_shape(), unknown_shape()},
                    /* input_tensors*/ {nullptr, tensor_1X6},

        }
        input_arg {
          name: "shape"
          type_attr: "Tshape"
        }
        output_arg {
          name: "output"
          type_attr: "T"
        }
        attr {
          name: "T"
          type: "type"
        }
        attr {
          name: "Tshape"
          type: "type"
          default_value {
            type: DT_INT32
          }

  %r1 = "tf.Reshape"(%in, %s1) {T = f32, Tshape = i32, device = ""} : (tensor<3x3x48x1xf32>, tensor<3xi32>) -> tensor<3x3x48xf32>
  %fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%r1, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<3x3x48xf32>, tensor<48xf32>, tensor<48xf32>) -> tensor<3x3x48xf32>
  %r2 = "tf.Reshape"(%fq, %s2) {T = f32, Tshape = i32, device = ""} : (tensor<3x3x48xf32>, tensor<4xi32>) -> tensor<3x3x48x1xf32>

  input: "BoxPredictor_4/ClassPredictor/act_quant/FakeQuantWithMinMaxVars"
  input: "BoxPredictor_4/Reshape_1/shape"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main

  input: "BoxPredictor_4/ClassPredictor/act_quant/FakeQuantWithMinMaxVars"
  input: "BoxPredictor_4/Reshape_1/shape"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main