"//tensorflow/c/eager:tfe_tensorhandle_internal",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:optional",
        "@com_google_absl//absl/types:variant",
    ],
)

cc_library(
    name = "parallel_device_lib",
    srcs = [":lib_sources"],
    hdrs = [":lib_headers"],
    visibility = ["//tensorflow:internal"],
    deps = [
        "//tensorflow/c:c_api",

        "//tensorflow/c/experimental/filesystem:filesystem_interface",
        "@com_github_googlecloudplatform_google_cloud_cpp//:storage_client",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:variant",
    ],
)

cc_library(
    name = "gcs_helper",
    srcs = ["gcs_helper.cc"],
    hdrs = ["gcs_helper.h"],
    linkstatic = 1,
    deps = [
        "//tensorflow/c:env",
    ],
)

  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,

    return tensorflow::ops::Identity(ctx, input, output, name);
  } else if (!DataTypeIsComplex(BaseType(dtype)) &&
             BaseType(dtype) != DT_VARIANT) {
    return errors::InvalidArgument(
        "Expected numeric or variant tensor, got dtype ", dtype);
  }
  return tensorflow::ops::Conj(ctx, input, output, name);
}

class AddGradientFunction : public GradientFunction {
 public:

    // Ownership of `aid` transferred to CallBackwardFunction below.
    Gradient* aid;
    if (output_tensor.GetDType() == tensorflow::DT_VARIANT) {
      // Note: Needs to be zeros rather than ones since there's currently no
      // ones_like for variants.
      aid = output_tensor.ZerosLike();
    } else {
      // TODO(allenl): Figure out why using zeros_like everywhere causes issues

            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",
            "@com_google_absl//absl/types:span",
            "@com_google_absl//absl/types:variant",
        ],
    }) + [
        ":abstract_tensor_handle",
        "//tensorflow/c:c_api_macros_hdrs",
        "//tensorflow/core:gpu_runtime",
        "//tensorflow/core/common_runtime/eager:eager_operation",

        "//tensorflow/core/platform:refcount",
    ] + if_not_mobile([
        "//tensorflow/core/kernels:tensor_list",
        "//tensorflow/core/kernels:tensor_list_util",
        "//tensorflow/core/kernels:variant_ops_util",
        "//tensorflow/core/kernels/data:optional_ops_util",
        "//tensorflow/core/platform:abi",
    ]),
    alwayslink = 1,
)

tf_cuda_library(
    name = "ops",
    srcs = [

        "composite" tensors, such as `tf.RaggedTensor`, as inputs.
    *   Fix device placement issues related to datasets with ragged tensors of
        strings (i.e. variant encoded data with types not supported on GPU).
    *   'experimental_follow_type_hints' for tf.function has been deprecated.
        Please use input_signature or reduce_retracing to minimize retracing.