}

// Check that we generate _handle_dtypes and _handle_shapes for the resource
// argument.

// CHECK:      op: "_Arg"

// CHECK:        key: "_handle_dtypes"
// CHECK-NEXT:   value {
// CHECK-NEXT:     list {
// CHECK-NEXT:       type: DT_FLOAT
// CHECK-NEXT:     }
// CHECK-NEXT:   }
// CHECK-NEXT: }

// CHECK:        key: "_handle_shapes"
// CHECK-NEXT:   value {
// CHECK-NEXT:     list {

    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "_handle_dtypes"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "_handle_shapes"
    value {
      list {
        shape {
          dim {
            size: 1001
          }
        }
      }
    }
  }
  attr {
    key: "index"
    value {
      i: 268
    }

    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "_handle_dtypes"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "_handle_shapes"
    value {
      list {
        shape {
        }
      }
    }
  }
  attr {
    key: "index"
    value {
      i: 1
    }
  }
}
node {
  name: "arg2"
  op: "_Arg"

    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "_handle_dtypes"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "_handle_shapes"
    value {
      list {
        shape {
          dim {
            size: 3
          }
          dim {
            size: 3
          }
          dim {
            size: 1
          }

                            handle_shapes_attr.mutable_list()->add_shape());
      }

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(
      SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr()));

  DataType dtype;

                            handle_shapes_attr.mutable_list()->add_shape());
      }

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(
      SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr()));

  DataType dtype;

}

bool IsResourceOutputShapesAttribute(const AttrValue& attr_value,
                                     llvm::StringRef attr_name) {
  if (attr_name == "_handle_dtypes" || attr_name == "_handle_shapes")
    return attr_value.value_case() == AttrValue::kList;
  return false;
}

void LoadImporterDialects(mlir::MLIRContext& context) {
  // Load dialects involved in the conversion

    return errors::InvalidArgument(
        "Mismatched resource types: ", DataTypeString(a.handle_type), " vs. ",
        DataTypeString(b.handle_type));
  }
  TF_RETURN_IF_ERROR(
      a.handle_shape.MergeWith(b.handle_shape, &result.handle_shape));
  return result;
}

  // Shape of the argument tensor.
  PartialTensorShape shape;

  // If the argument is a resource variable, the type and shape of the
  // variable's value.
  DataType handle_type = DT_INVALID;
  PartialTensorShape handle_shape;
};
typedef std::unordered_map<string, std::vector<InferredShape>> GraphShapeInfo;

// Infer shapes for all Tensors in a graph, and save them in a map.  The vector

  EXPECT_NE(iter, shape_info.end());
  EXPECT_EQ(iter->second.size(), 1);
  EXPECT_EQ(iter->second.at(0).handle_type, DT_FLOAT);
  TensorShape resource_shape;
  EXPECT_TRUE(iter->second.at(0).handle_shape.AsTensorShape(&resource_shape));
  EXPECT_EQ(resource_shape, TensorShape({2, 3}));
}

}  // namespace