"//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/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 = [

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable

  "$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:

            "//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",

#include "tensorflow/c/experimental/filesystem/plugins/gcs/gcs_filesystem.h"

#include <stdlib.h>
#include <string.h>

#include <variant>

#include "absl/strings/numbers.h"
#include "absl/strings/str_cat.h"
#include "absl/types/variant.h"
#include "google/cloud/storage/client.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/experimental/filesystem/plugins/gcs/gcs_helper.h"

        "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.

        "//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",
    ],
)

  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:
      return true;
    case DT_QINT8:
    case DT_QINT16:
    case DT_QINT32:
    case DT_QUINT8:
    case DT_QUINT16:
      return tensorflow::flags::Global()