#     release_cpu_macos:               Toolchain and CUDA options for MacOS CPU builds.
#     release_cpu_windows:             Toolchain and CUDA options for Windows CPU builds.

# Default build options. These are applied first and unconditionally.

# For projects which use TensorFlow as part of a Bazel build process, putting
# nothing in a bazelrc will default to a monolithic build. The following line

<code>min</code> or <code>max</code> applied to constant arguments,
<code>unsafe.Sizeof</code> applied to <a href="#Package_unsafe">certain values</a>,
<code>cap</code> or <code>len</code> applied to
<a href="#Length_and_capacity">some expressions</a>,
<code>real</code> and <code>imag</code> applied to a complex constant
and <code>complex</code> applied to numeric constants.

without being bound by section 3 of the GNU GPL.

  2. Conveying Modified Versions.

  If you modify a copy of the Library, and, in your modifications, a
facility refers to a function or data to be supplied by an Application
that uses the facility (other than as an argument passed when the
facility is invoked), then you may convey a copy of the modified
version:

	}

	if fi.Deleted {
		return ObjectInfo{}, toObjectErr(errMethodNotAllowed, bucket, object)
	}

	filterOnlineDisksInplace(fi, metaArr, onlineDisks)

	// if version-id is not specified retention is supposed to be set on the latest object.
	if opts.VersionID == "" {
		opts.VersionID = fi.VersionID
	}

	objInfo := fi.ToObjectInfo(bucket, object, opts.Versioned || opts.VersionSuspended)

// container and canned data from https://github.com/minio/minio-ldap-testing
//
// Each set of client app params corresponds to a separate openid server, and
// the i-th server in this will be applied the i-th policy in `rolePolicies`. If
// a rolePolicies entry is an empty string, that server will be configured as
// policy-claim based openid server. NOTE that a valid configuration can have a

        causes the returned value to be the local SSIM map instead of the global
        mean.

*   TF Core:

    *   `tf.custom_gradient` can now be applied to functions that accept
        "composite" tensors, such as `tf.RaggedTensor`, as inputs.
    *   Fix device placement issues related to datasets with ragged tensors of

        * This is useful, for example, to require authentication or permissions in specific group of *path operations*.
        * Different `dependencies` can be applied to different routers.
    * These `dependencies` are run before the normal parameter dependencies. And normal dependencies are run too. They can be combined.