TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Try to set an invalid shape (cannot change 2x3 to a 2x5).
  dims[1] = 5;
  TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Test for a scalar.
  TF_Operation* three = ScalarConst(3, graph, s);

tf_cc_test(
    name = "modular_filesystem_test",
    size = "small",
    srcs = ["modular_filesystem_test.cc"],
    linkopts = ["-ldl"],
    tags = [
        "manual",  # Requires DSOs as arguments, eventual setup
        "notap",  # b/139060984, requires implementing modular support for Google filesystem
    ],
    deps = [
        ":modular_filesystem",
        "//tensorflow/core:framework_internal",

*   Extends `tf.keras.layers.Lambda` layers to support multi-argument lambdas,
    and keyword arguments when calling the layer.
*   Functional models now get constructed if *any* tensor in a layer call's
    arguments/keyword arguments comes from a keras input. Previously the
    functional api would only work if all of the elements in the first argument
    to the layer came from a keras input.

                                               TF_Status* status,
                                               void* device_info);

  // Method to execute an operation.
  //
  // Arguments provide enough information to reconstruct the original `TFE_Op`,
  // or construct a transformed version, by inspecting the passed `op`.
  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may

# limitations under the License.
# ==============================================================================
#
# Usage: rename_and_verify_wheels.sh
# This script is aware of TFCI_ variables, so it doesn't need any arguments.
# Puts new wheel through auditwheel to rename and verify it, deletes the old
# one, checks the filesize, and then ensures the new wheel is installable.
set -euxo pipefail

cd "$TFCI_OUTPUT_DIR"

    tensorflow::ImmediateExecutionOperation* op =
        tensorflow::unwrap(op_to_reset);
    op->Clear();
    status->status = op->Reset(op_or_function_name, raw_device_name);
  } else {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "op_to_reset should not be nullptr");
  }
}

void TFE_ContextEnableGraphCollection(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(true);
}

        tensorflow::errors::InvalidArgument("Unparseable AttrValue proto");
    return;
  }
  if (op == nullptr) {
    status->status = tensorflow::errors::InvalidArgument(
        "Got a null or uninitialized `op` argument");
    return;
  }
  tensorflow::EagerOperation* operation =
      OperationFromInterface(tensorflow::unwrap(const_cast<TFE_Op*>(op)));
  operation->MutableAttrs()->Set(attr_name, attr_value);
}

      (*result)[id].push_back(output_gradients[i]);
    }
  }
  return absl::OkStatus();
}

// TODO(agarwal): use an automatic mechanism for handling None arguments to
// gradient functions.
//
// Some gradient functions can accept None arguments for gradients. The
// following maps the operation name to the indices at which the corresponding
// gradient function can accept None values. e.g. FusedBatchNorm outputs 5

    return;
  } else if (S_ISDIR(st.st_mode)) {
    TF_SetStatus(status, TF_FAILED_PRECONDITION, "source path is a directory");
    return;
  }

  // Do the actual rename. Here both arguments are filenames.
  if (rename(src, dst) != 0)
    TF_SetStatusFromIOError(status, errno, dst);
  else
    TF_SetStatus(status, TF_OK, "");
}

# Sets the default Apple platform to macOS.
build:macos --apple_platform_type=macos

# gRPC on MacOS requires this #define
build:macos --copt=-DGRPC_BAZEL_BUILD

# Avoid hitting command line argument limit
build:macos --features=archive_param_file

# Settings for MacOS on ARM CPUs.
build:macos_arm64 --cpu=darwin_arm64
build:macos_arm64 --macos_minimum_os=11.0