<!-- Autogenerated by mlir-tblgen; don't manually edit -->
### `-prepare-tpu-computation-for-tf-export`: Prepare TPU computation to be legal for export to TensorFlow
Prepares TPU computation module attached to _TPUCompileMlir op for
TensorFlow graph export by making transformation such as replacing or
removing MLIR or XLA specific attributes that are not legal in TensorFlow
graph.
### `-tf-device-cluster-outlining`: Outlines regions of tf_device.cluster operations
This pass outlines the body of a `tf_device.cluster` into a function and
replaces the `tf_device.cluster` op with an equivalent `tf_device.cluster_func`
op. Implicit operands will be captured and materialized as explicit arguments to
the newly created functions and associated `tf_device.cluster_func` ops.

For example, the following:

```mlir
func @computation(%arg0: tensor<i32>) -> tensor<i32> {
  %cluster = "tf_device.cluster"() ( {
    %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
    tf_device.return %identity : tensor<i32>
  }) : () -> (tensor<i32>)
  return %cluster : tensor<i32>
}
```

will be transformed into:

```mlir
func @computation(%arg0: tensor<i32>) -> tensor<i32> {
  %cluster = "tf_device.cluster_func"(%arg0) {func = @_func} : (tensor<i32>) -> tensor<i32>
  return %cluster : tensor<i32>
}

func @_func(%arg0: tensor<i32>) -> tensor<i32> {
  %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
  return %identity : tensor<i32>
}
```
### `-tf-device-constant-sinking`: Sinks constants implicitly captured in a tf_device.cluster region.
This pass sinks implicitly captured constants (`tf.Const` ops) used by and into
a `tf_device.cluster` region. Performing this prior to outlining will reduce the
number of arguments of the outlined function.

For example, the following:

```mlir
func @cluster() -> tensor<i32> {
  %const = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %cluster = "tf_device.cluster"() ( {
    %identity = "tf.Identity"(%const) : (tensor<i32>) -> tensor<i32>
    tf_device.return %identity : tensor<i32>
  }) : () -> (tensor<i32>)
  return %cluster : tensor<i32>
}
```

will be transformed into:

```mlir
func @cluster() -> tensor<i32> {
  %cluster = "tf_device.cluster"() ( {
    %const = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %identity = "tf.Identity"(%const) : (tensor<i32>) -> tensor<i32>
    tf_device.return %identity : tensor<i32>
  }) : () -> (tensor<i32>)
  return %cluster : tensor<i32>
}
```
### `-tf-device-convert-launch-func-to-tf-call`: Rewrites tf_device::LaunchFuncOp to TF::PartitionedCallOp
This pass converts tf_device::LaunchFuncOp into an equivalent
TF::PartitionedCallOp so that it can be exported to TensorFlow GraphDef.
### `-tf-device-launch-outlining`: Outlines regions of tf_device.launch operations
This pass outlines the body of a `tf_device.launch` into a function and
replaces the `tf_device.launch` op with an equivalent `tf_device.launch_func`
op. Implicit operands will be captured and materialized as explicit arguments to
the newly created functions and associated `tf_device.launch_func` ops. The
`device` attribute from the `launch` op is transferred to `launch_func`.

For example, the following:

```mlir
func @computation(%arg0: tensor<i32>) -> tensor<i32> {
  %launch = "tf_device.launch"() ( {
    %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
    tf_device.return %identity : tensor<i32>
  }) {device = "some_device"} : () -> (tensor<i32>)
  return %launch : tensor<i32>
}
```

will be transformed into:

```mlir
func @computation(%arg0: tensor<i32>) -> tensor<i32> {
  %launch = "tf_device.launch_func"(%arg0) {device = "some_device", func = @_func} : (tensor<i32>) -> tensor<i32>
  return %launch : tensor<i32>
}

func @_func(%arg0: tensor<i32>) -> tensor<i32> {
  %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
  return %identity : tensor<i32>
}
```
### `-tf-device-mark-input-output-aliases`: Marks device cluster inputs-output pairs that read/write to the same variable as aliases
This pass analyzes the inputs and outputs to device cluster and marks those
input-output pairs as aliases (using `tf.aliasing_output` attribute) which read
and write to the same resource. This aliasing information can then be propagated
to XLA compiler for input/output buffer space optimizations.
### `-tf-drop-while-shape-invariant`: Drop `shape_invariant` attrbute from While/WhileRegion ops.
Drop `shape_invariant` attribute from tf.While and tf.WhileRegion op. This
would allow shape inference pass to further refine operand/result shapes of
these ops. This is only safe to do when compiling to XLA.
### `-tf-drop-while-shape-invariant-in-device-cluster`: Drop `shape_invariant` attrbute from While/WhileRegion ops inside device cluster.
Drop `shape_invariant` attribute from tf.While and tf.WhileRegion op only
inside device cluster. This would allow shape inference pass to further
refine operand/result shapes of these ops. This is only safe to do when
compiling to XLA.
### `-tf-executor-graph-pruning`: Prunes unreachable ops in a tf_executor.graph
This pass removes ops from a `tf_executor.graph` that are not transitively, via
data or control dependencies, connected to the associated `tf_executor.fetch`
op. The order of ops will be preserved. Functions named `main` with no
`tf.entry_function` attribute will not be pruned, as such graphs/functions may
have been imported from a V1 TensorFlow graph, where feeds/fetches/targets are
not provided at certain stages of IR transformation (e.g. pre-placement).

Option `ops-to-preserve` allows to specify ops that should not be pruned,
regardless of their reachability.

For example, the following:

```mlir
func @graph(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
  %graph = tf_executor.graph {
    %transitive_reachable_data:2 = tf_executor.island wraps "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
    %reachable_data:2 = tf_executor.island wraps "tf.Identity"(%transitive_reachable_data#0) : (tensor<i32>) -> tensor<i32>
    %unreachable_data:2 = tf_executor.island wraps "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %transitive_reachable_control = tf_executor.island wraps "tf.NoOp"() : () -> ()
    %reachable_control = tf_executor.island(%transitive_reachable_control) wraps "tf.NoOp"() : () -> ()
    %unreachable_control = tf_executor.island wraps "tf.NoOp"() : () -> tensor<i32>
    tf_executor.fetch %reachable_data#0, %reachable_control : tensor<i32>, !tf_executor.control
  }
  return %graph : tensor<i32>
}
```

will be transformed into:

```mlir
func @graph(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
  %graph = tf_executor.graph {
    %transitive_reachable_data:2 = tf_executor.island wraps "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
    %reachable_data:2 = tf_executor.island wraps "tf.Identity"(%transitive_reachable_data#0) : (tensor<i32>) -> tensor<i32>
    %transitive_reachable_control = tf_executor.island wraps "tf.NoOp"() : () -> ()
    %reachable_control = tf_executor.island(%transitive_reachable_control) wraps "tf.NoOp"() : () -> ()
    tf_executor.fetch %reachable_data#0, %reachable_control : tensor<i32>, !tf_executor.control
  }
  return %graph : tensor<i32>
}
```

#### Options
```
-ops-to-preserve : Comma separated list of ops that should not be pruned regardless of reachability
```
### `-tf-executor-island-coarsening`: Walks tf_executor::GraphOp and merges individual tf_executor::IslandOps.
This pass performs whole graph analysis for a graph encapsulated into tf_executor::GraphOp.
The analysis identifies all IslandOps within the graph which could be merged together.
The goal is to merge as many islands as possible.
Once analysis is completed, the pass merges all IslandOps in a single scan.
### `-tf-executor-to-functional-conversion`: Lifts tf_executor.island inner ops from a tf_executor.graph
This pass converts tf_executor.graphs consisting of only tf_executor.islands and
a tf_executor.fetch into a sea of nodes consisting of TensorFlow Dialect ops by
lifting such ops out of a tf_executor.graph's tf_executor.islands. If V1 control
flow ops are present in a tf_executor.graph, an error will be returned.

For example, the following:

```mlir
func @my_fn(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %graph_results:2 = tf_executor.graph {
    %island_0_result, %island_0_control = tf_executor.island {
      %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %identity : tensor<i32>
    }
    %island_1_result, %island_1_control = tf_executor.island {
      %identity_n:2 = "tf.IdentityN"(%arg1, %island_0_result) : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
      tf_executor.yield %identity_n#0
    }
    tf_executor.fetch %island_0_result, %island_1_result : tensor<i32>, tensor<i32>
  }
  return %graph_results#0, %graph_results#1 : tensor<i32>, tensor<i32>
}
```

will be transformed into:

```mlir
func @my_fn(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
  %identity_n:2 = "tf.IdentityN"(%arg1, %identity) : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
  return %identity, %identity_n#0 : tensor<i32>, tensor<i32>
}
```
### `-tf-executor-tpu-v1-island-inlining`: Inline calls to the nested TPU module.
This pass inlines the islands calling into the nested module that was
outlined, thus reversing the effect of the
`-tf-executor-tpu-v1-island-outlining` pass.

For example, the following:
```mlir
module {
  func @foo(%arg0: tensor<f32>) -> tensor<f32> {
    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.PartitionedCall"(%arg0) {f = @_tpu_v1_compat_outlined::@bar} : (tensor<f32>) -> tensor<f32>
      tf_executor.fetch %outputs : tensor<f32>
    }
    return %0 : tensor<f32>
  }
  module @_tpu_v1_compat_outlined {
    func nested @bar(%arg0: tensor<f32>) -> tensor<f32> {
      %0 = "tf.opA"(%arg0) : (tensor<f32>) -> tensor<f32>
      return %0 : tensor<f32>
    }
  }
}
```

will be transformed into:

```mlir
module  {
  func @foo(%arg0: tensor<f32>) -> tensor<f32> {
    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island {
        %1 = "tf.opA"(%arg0) : (tensor<f32>) -> tensor<f32>
        tf_executor.yield %1 : tensor<f32>
      }
      tf_executor.fetch %outputs : tensor<f32>
    }
    return %0 : tensor<f32>
  }
}
```
### `-tf-functional-control-flow-to-regions`: Transforms functional control flow operations to their region-based counterparts
This pass transforms functional control flow operations in the TensorFlow
dialect to their region-based counterparts, i.e., `tf.If` is transformed to
`tf.IfRegion` and `tf.While` is transformed to `tf.WhileRegion`.

For example, this functional operation

```mlir
  %0 = "tf.If"(%arg0, %arg1) {
    then_branch = @then_branch_func, else_branch = @else_branch_func, is_stateless = false
  } : (tensor<i1>, tensor<*xf32>) -> tensor<*xf32>
```

will be transformed into this region-based operation

```mlir
    %0 = "tf.IfRegion"(%arg0) ( {
      %1 = call @then_branch_func(%arg1) : (tensor<*xf32>) -> tensor<*xf32>
      "tf.Yield"(%1) : (tensor<*xf32>) -> ()
    },  {
      %1 = call @else_branch_func(%arg1) : (tensor<*xf32>) -> tensor<*xf32>
      "tf.Yield"(%1) : (tensor<*xf32>) -> ()
    }) {is_stateless = false} : (tensor<i1>) -> tensor<*xf32>
```
### `-tf-gpu-op-fusion`: Fusion optimization for GPU targets
This pass is performing fusion specific to GPU targets. This is an ad-hoc
pass for now, but should be integrated with some notion of "target" in the
MLIR pipeline in the future.
### `-tf-hoist-replicate-invariant-resource-writes`: Hoists writes to replicate invariant resource variables.
This pass hoists replicate invariant resource variable writes outside
tf_device.replicate op. These may have been inserted by other passes such as
resource op lifting. However, if the resource variable is not replicated, writes
to such variables for each replica are redundant and can be replaced by writing
a single value from first replica.

The benefit of this optimization is reduced memory requirement on host. For
multiple writes (one from each replica) to such variables, the host would
allocate buffer space to recieve the device output from all replicas, which is
not required. We can use the output of first replica in such cases.
### `-tf-lower-quantized`: Lowers ops that require quantized input or output.
This pass rewrites all ops that have at least one input or output that must
be a quantized type to ops whose inputs and outputs allow non-quantized
types. Examples of quantized types are TF_Qint8 or TF_Quint8.

An example is TF_DequantizeOp, which converts a quantized type to a float.
This op is rewritten to generic ops that perform the scale and shift
and can operate on non-quantized types.

Currently, TF_DequantizeOp is the only op with a lowering that falls
in this category. When more lowerings are added (e.g. QuantizeV2Op),
they should be added to this pass.
### `-tf-mark-ops-for-outside-compilation`: Marks ops in device cluster for outside compilation if they are unsupported on device.
This pass marks unsupported ops in a device cluster with
`_xla_outside_compilation` attribute so the operations will run on the host
instead of the device. Unsupported ops are ops that can not be code
generated to run on the device for the cluster including:

1. String operations on TPUs.
2. Operations that don't have a kernel defined for the device.

This pass is conservative in that it will mark all ops for outside compilation
that can not be compiled for the device.  Exceptions for this are added for ops
that will be rewritten or decomposed before compiling on device.


For example, tf_device.cluster op with an unsupported op, tf.UnsupportedOp:

```mlir
func @unsupported_op() -> tensor<i32> {
  %0 = "tf_device.cluster"() ( {
    %1 = "tf.UnsupportedOp"() : () -> tensor<i32>
    %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
    tf_device.return %2 : tensor<i32>
  }) {allow_soft_placement = true, num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> tensor<i32>
  return %0 : tensor<i32>
}
```

will mark tf.UnsupportedOp with `_xla_outside_compilation` attribute:

```mlir
func @unsupported_op() -> tensor<i32> {
  %0 = "tf_device.cluster"() ( {
    %1 = "tf.UnsupportedOp"() {_xla_outside_compilation = "auto0"} : () -> tensor<i32>
    %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
    tf_device.return %2 : tensor<i32>
  }) {allow_soft_placement = true, device_assignment = [], num_cores_per_replica = 1 : i64, topology = ""} : () -> tensor<i32>
  return %0 : tensor<i32>
}
```
### `-tf-materialize-passthrough-op`: Materialize the MlirPassthroughOp by replacing it with the MLIR module attached as an attribute
### `-tf-merge-control-flow`: Merges IfRegion ops together with a common predicate.
This pass merges IfRegion ops together if they have the same predicate and it
is safe to do so (there are no intermediate dependencies, they are in the
same block, etc).

For example:

```mlir
"tf.IfRegion"(%0) ( {
  %2 = "tf.A"() : () -> (tensor<f32>)
  "tf.Yield"() : () -> ()
  }, {
  "tf.Yield"() : () -> ()
 }) { is_stateless = true } : (tensor<i1>) -> ()
"tf.IfRegion"(%0) ( {
  %2 = "tf.B"() : () -> (tensor<f32>)
  "tf.Yield"() : () -> ()
  }, {
  "tf.Yield"() : () -> ()
  }) { is_stateless = true } : (tensor<i1>) -> ()
```

Would be transformed to:

```mlir
"tf.IfRegion"(%0) ( {
  %2 = "tf.A"() : () -> (tensor<f32>)
  %3 = "tf.B"() : () -> (tensor<f32>)
  "tf.Yield"() : () -> ()
  }, {
  "tf.Yield"() : () -> ()
  }) { is_stateless = true } : (tensor<i1>) -> ()
```
### `-tf-optimize`: Optimize TensorFlow module
### `-tf-outside-compiled-to-host-launch`: Wraps each op with the _xla_outside_compiled attribute in a separate tf_device.launch on replicated host device.
This pass wraps ops with the same `_xla_outside_compilation`
attribute value in a tf_device.launch op with host device assignment.

A simple example:

```mlir
  "tf_device.cluster"() ( {
    "tf.A"()
    "tf.B"() {_xla_outside_compilation = "cluster1"}
    "tf.C"()
    tf_device.return
  }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []}
```

Would become the following ops (unimportant attribute, type are omitted):

```mlir
  "tf_device.cluster"() ( {
    "tf.A"()
    "tf_device.launch"() {
      "tf.B"() {_xla_outside_compilation = "cluster1"}
      tf_device.return
    } {device = "TPU_REPLICATED_HOST"} : () -> ()
    "tf.C"()
    tf_device.return
  }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []}
```
### `-tf-promote-resources-to-args`: Promote resources reads/writes to function inputs/outputs.
This pass promotes resource accesses in the main function to input arguments
and outputs of the main function.

Two types of resources are supported:
(1) A function argument of TF::ResourceType type (this pass).
(2) A VarHandleOp in the function (tf-promote-var-handles-to-args).

After the pass,

 . The function will have an input argument for each resource that is
   already provided as an input argument or is read. The type of the input
   argument will become the shape of the value represented by the resource.

 . The function will have an output for each resource that is written. The
   type of the output will become the shape of the resource.

The information of variable identification and input-output alising is
recorded as named attributes of the input argument or output:

 . 'tf.resource_name' matches 'shared_name' of VarHandleOp, which represents
   the identifier of the corresponding resource. This attribute is added to
   an input argument if the initial value of the resource is read, or to the
   output if the initial value is not read.

 . 'tf.aliasing_output' is the index of the function output that is an alias
   of the input argument. This attribute is added only to the input argument
   when the initial value of the corresponding resource is read, and the
   resource is written later.

Assumption of this pass:
 . Compound resource operations have already been decomposed.
 . Dead functions have already been removed, as resource arguments in dead
   functions can cause the pass to fail.
### `-tf-promote-var-handles-to-args`: Promote tf.VarHandleOps to function arguments.
See joint description in promote resources to args.### `-tf-region-control-flow-to-functional`: Transforms region-based control flow operations to their functional counterparts
This pass transforms region-based control flow operations in the TensorFlow
dialect to their functional counterparts, i.e., `tf.IfRegion` is transformed to
`tf.If` and `tf.WhileRegion` is transformed to `tf.While`.

For example, this region-based operation

```mlir
    %0 = "tf.IfRegion"(%arg0) ( {
      %1 = call @then_branch_func(%arg1) : (tensor<*xf32>) -> tensor<*xf32>
      "tf.Yield"(%1) : (tensor<*xf32>) -> ()
    },  {
      %1 = call @else_branch_func(%arg1) : (tensor<*xf32>) -> tensor<*xf32>
      "tf.Yield"(%1) : (tensor<*xf32>) -> ()
    }) {is_stateless = false} : (tensor<i1>) -> tensor<*xf32>
```

will be transformed into this functional operation

```mlir
  %0 = "tf.If"(%arg0, %arg1) {
    then_branch = @then_branch_func, else_branch = @else_branch_func, is_stateless = false
  } : (tensor<i1>, tensor<*xf32>) -> tensor<*xf32>
```
### `-tf-replicate-invariant-op-hoisting`: Hoists replicate invariant operations out of replicate
This pass looks for replicate invariant ops in a `tf_device.replicate` op
region and hoists them out. It also makes `tf.Shape` ops replicate invariant
if possible. This currently updates or replaces `tf.Shape` ops of replicated
arguments, either tensors or resources.

For example, the following

```mlir
tf_device.replicate([%0, %1] as %ri: tensor<*xi32>) {n = 2 : i32} {
  %2 = "tf.Shape"(%ri) : (tensor<*xi32>) -> tensor<?xi32>
  tf_device.return
}
```

gets converted to

```mlir
tf_device.replicate([%0, %1] as %ri: tensor<*xi32>) {n = 2 : i32} {
  %2 = "tf.Shape"(%0) : (tensor<*xi32>) -> tensor<?xi32>
  tf_device.return
}
```

and for resource variables the following

```mlir
tf_device.replicate([%0, %1] as %ri: tensor<*x!tf_type.resource>) {n = 2 : i32} {
  %2 = "tf.ReadVariableOp"(%ri) : tensor<*x!tf_type.resource> -> tensor<*xi32>
  %3 = "tf.Shape"(%2) : (tensor<*xi32>) -> tensor<?xi32>
  tf_device.return
}
```

gets converted to

```mlir
tf_device.replicate([%0, %1] as %ri: tensor<*x!tf_type.resource>) {n = 2 : i32} {
  %2 = "tf.ReadVariableOp"(%ri) : tensor<*x!tf_type.resource> -> tensor<*xi32>
  %3 = "tf.VariableShape"(%0) : (tensor<*x!tf_type.resource>) -> tensor<?xi32>
  tf_device.return
}
```
### `-tf-shape-inference`: Simple Shape Inference on TensorFlow Dialect

#### Options
```
-max-iterations : Maximum shape inference iterations
```
### `-tf-tensor-array-ops-decomposition`: Decompose tensor array operations into local variable operations.
A pass that converts tensor array operations to tensor operations and
read/assign ops on local variables. A later resource lifting pass can further
remove the local variables.

This pass requires that the full shape of the tensor array can be inferred:
1) the size needs to be a constant, 2) it specifies the full element shape,
or that can be inferred from a later write, and 3) all elements have the same
shape.
### `-tf-tensor-device-copy`: Fold the tf.Identity op and the tf.IdentityN op if the op has the same device as its operand
### `-tf-tensor-list-ops-decomposition`: Decomposes TensorList operations into generic operations on tensors.
This pass rewrites TensorList operations into generic and non-mutating
operations on tensors. This results in operations that can be legalized to XLA.

The list is converted to a single large tensor that includes all list elements,
with a new first dimension for the list index. List update operations are
converted to operations that create a new tensor representing the list.

In the current implementation, the resulting operations are statically shaped,
which means it must be possible to infer a bound on the full shape of the
TensorList. That is, the `element_shape` and `num_elements` arguments to a
tensor list creation op are constant.

A tensor list creation op `tf.EmptyTensorList`/`tf.TensorListReserve` will be
turned in to a zero-initialized buffer, and the size is initialized to 0
for `tf.EmptyTensorList` or the specified size for `tf.TensorListReserve`.
Each push will be turned into `tf.XlaDynamicUpdateSlice` with the incremented
size, and each pop will be turned into a `tf.Slice` and a copy of the buffer
with decremented size. Each `tf.TensorListSetItem` will be turned into a
`tf.XlaDynamicUpdateSlice` with unchanged size, and each `tf.TensorListGetItem`
will be rewritten to a `tf.Slice`.

The pass also works across control flow and functional calls.

For example, the TensorList ops in the following function:

```mlir
func @main(%arg0: tensor<8x4xf32>) {
  %elem_shape = "tf.Const"() {value = dense<[8, 4]> : tensor<2xi32>} : () -> tensor<2xi32>
  %max_size = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
  %tl = "tf.EmptyTensorList"(%elem_shape, %max_size) : (tensor<2xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<8x4xf32>>>
  %push = "tf.TensorListPushBack"(%tl, %arg0) : (tensor<!tf_type.variant<tensor<8x4xf32>>>, tensor<8x4xf32>) -> tensor<!tf_type.variant<tensor<8x4xf32>>>
  return
}
```

will be transformed to:

```mlir
func @main(%arg0: tensor<8x4xf32>) {
  // EmptyTensorList lowering
  %emptyi = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %emptyf = "tf.Cast"(%emptyi) : (tensor<i32>) -> tensor<f32>
  %size_shape = "tf.Const"() {value = dense<[10, 8, 4]> : tensor<3xi32>} : () -> tensor<3xi32>
  %tl = "tf.BroadcastTo"(%emptyf, %size_shape) : (tensor<f32>, tensor<3xi32>) -> tensor<10x8x4xf32>
  // TensorListPushBack lowering
  %index_in_list = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
  %arg0_shape = "tf.Const"() {value = dense<[1, 8, 4]> : tensor<3xi32>} : () -> tensor<3xi32>
  %arg0_reshaped = "tf.Reshape"(%arg0, %arg0_shape) : (tensor<8x4xf32>, tensor<3xi32>) -> tensor<1x8x4xf32>
  %zeroi2 = "tf.Const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>
  %axis = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %start_indices = "tf.ConcatV2"(%index_in_list, %zeroi2, %axis) : (tensor<1xi32>, tensor<2xi32>, tensor<i32>) -> tensor<3xi32>
  %push = "tf.XlaDynamicUpdateSlice"(%tl, %arg0_reshaped, %start_indices) : (tensor<10x8x4xf32>, tensor<1x8x4xf32>, tensor<3xi32>) -> tensor<10x8x4xf32>
  %one = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %next_index_in_list = "tf.AddV2"(%index_in_list, %one) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  return
}
```
### `-tf-tpu-cleanup-cluster-attributes`: Eliminate _tpu_replicate and other attributes from ops in a cluster
This pass eliminate `_tpu_replicate` and `device` attribute on operations
that are contained in a tf_device.cluster op.
### `-tf-tpu-cluster-formation`: Forms clusters from operations assigned to the same TPU computation
TPU computations from the frontend are composed of a `tf.TPUReplicateMetadata`
op, a subgraph of ops (TensorFlow Dialect) each with a matching `_tpu_replicate`
attribute relative to the associated `tf.TPUReplicateMetadata` op, and
optionally `tf.TPUReplicatedInput` and `tf.TPUReplicatedOutput` ops feeding in
inputs and outputs to and from a replicated TPU computation. The number of times
a TPU computation is replicated is defined in the `tf.TPUReplicateMetadata` op
(`num_replicas` attribute) and operand and result sizes of
`tf.TPUReplicatedInput` and `tf.TPUReplicatedOutput` respectively must match,
excluding packed tensors. It is also assumed ops of the same TPU computation do
not have ops outside of the TPU computation that are both inputs and outputs to
the same TPU computation.

This pass takes the TPU computation subgraph, moves them into a
`tf_device.cluster`, and copies over attributes from the associated
`tf.TPUReplicateMetadata` op to the newly created `tf_device.cluster`. If the
computation is replicated (`num_replicas` > 1), the `num_replicas` attribute is
not copied over but instead the `tf_device.cluster` is further wrapped with a
`tf_device.replicate`, and associated `tf.TPUReplicatedInput` and
`tf.TPUReplicatedOutput` ops are replaced as the `tf_device.replicate` operands
and results. Otherwise, the single operands and results of the associated
`tf.TPUReplicatedInput` and `tf.TPUReplicatedOutput` ops are simply forwarded to
the `tf_device.cluster`.

For example, the following non replicated computation:

```mlir
func @tpu_computation(%arg0: tensor<i32>) -> tensor<i32> {
  // Metadata op for cluster `cluster` with 1 replica, 1 core per replica and
  // with topology `<topology>`.
  "tf.TPUReplicateMetadata"() {_tpu_replicate = "cluster", num_relicas = 1, num_cores_per_replica = 1, topology = "<topology>", device_assignment = [], padding_map = []} : () -> ()
  %replicated_input = "tf.TPUReplicatedInput"(%arg0) : (tensor<i32>) -> tensor<i32>
  %identity = "tf.Identity"(%replicated_input) {_tpu_replicate = "cluster"} : (tensor<i32>) -> tensor<i32>
  %replicated_output = "tf.TPUReplicatedOutput(%identity) : (tensor<i32>) -> tensor<i32>
  return %replicated_output : tensor<i32>
}
```

will be transformed into:

```mlir
func @tpu_computation(%arg0: tensor<i32>) -> tensor<i32> {
  %cluster = "tf_device.cluster"() ( {
    %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
    tf_device.return %identity : tensor<i32>
  }) {_tpu_replicate = "cluster", num_cores_per_replica = 1, topology = "topology", device_assignment = [], padding_map = []} : () -> (tensor<i32>)
  return %cluster : tensor<i32>
}
```

The following replicated computation:

```mlir
func @tpu_computation(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  "tf.TPUReplicateMetadata"() {_tpu_replicate = "cluster", num_relicas = 2, num_cores_per_replica = 1, topology = "topology", device_assignment = [], padding_map = []} : () -> ()
  %replicated_input = "tf.TPUReplicatedInput"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %identity = "tf.Identity"(%replicated_input) {_tpu_replicate = "cluster"} : (tensor<i32>) -> tensor<i32>
  %replicated_output:2 = "tf.TPUReplicatedOutput(%identity) : (tensor<i32>) -> (tensor<i32>, tensor<i32>)
  return %replicated_output#0, %replicated_output#1 : tensor<i32>, tensor<i32>
}
```

will be transformed into:

```mlir
func @tpu_computation(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %replicate:2 = tf_device.replicate([%arg0, %arg1] as %replicated_input) {n = 2 : i32} {
    %cluster = "tf_device.cluster"() ( {
      %identity = "tf.Identity"(%replicated_input) : (tensor<i32>) -> tensor<i32>
      tf_device.return %identity : tensor<i32>
    }) {_tpu_replicate = "cluster", num_cores_per_replica = 1, topology = "topology", device_assignment = [], padding_map = []} : () -> (tensor<i32>)
    tf_device.return %cluster : tensor<i32>
  }
  return %replicate#0, %replicate#1 : tensor<i32>, tensor<i32>
}
```
### `-tf-tpu-extract-head-tail-outside-compilation`: Extracts TPU head or tail outside compilation to separate host launches before/after device cluster.
This pass extracts a CPU computation cluster with `_xla_outside_compilation`
annotation from the head or tail of a TPU cluster.

For example:

```mlir
  %cluster = "tf_device.cluster"() ( {
    %a = "tf.A"(%arg0) {_xla_outside_compilation = "cluster1"} : (tensor<i32>) -> tensor<i32>
    %b = "tf.B"(%a) : (tensor<i32>) -> tensor<i32>
    %c = "tf.C"(%b) {_xla_outside_compilation = "cluster1"} : (tensor<i32>) -> tensor<i32>
    tf_device.return %c : tensor<i32>
  }) {num_cores_per_replica = 1, step_marker_location = "", padding_map = [], topology = "", device_assignment = []} : () -> tensor<i32>
  return %cluster : tensor<i32>
```

becomes:

```mlir
%0 = "tf_device.launch"() ( {
  %3 = "tf.A"(%arg0) : (tensor<i32>) -> tensor<i32>
  tf_device.return %3 : tensor<i32>
}) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : () -> tensor<i32>
%1 = "tf_device.cluster"() ( {
  %3 = "tf.B"(%0) : (tensor<i32>) -> tensor<i32>
  tf_device.return %3 : tensor<i32>
}) {device_assignment = [], num_cores_per_replica = 1 : i64, padding_map = [], step_marker_location = "", topology = ""} : () -> tensor<i32>
%2 = "tf_device.launch"() ( {
  %3 = "tf.C"(%1) : (tensor<i32>) -> tensor<i32>
  tf_device.return %3 : tensor<i32>
}) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : () -> tensor<i32>
return %2 : tensor<i32>

```
### `-tf-tpu-extract-outside-compilation`: Extracts TPU outside compilation computation to a separate tf_device.parallel_execute region.
This pass extracts a CPU computation cluster with `_xla_outside_compilation`
annotation, which denotes ops that should be run on CPU/host, from a TPU cluster.
Each outside compilation cluster is moved to
a tf_device.parallel_execute region. The TPU cluster is also moved to a
tf_device.parallel_execute region. Communication ops between device and host are
added to pass inputs/outputs to/from the outside compiled region.

For example, the following tf_device.cluster with an op marked for `xla_outside_compilation`:

```mlir
func @outside_compilation() -> tensor<f32> {
  %0 = "tf_device.cluster"() ( {
    %1 = "tf.Const"() {_xla_outside_compilation = "0", value = dense<1.0> : tensor<f32>} : () -> (tensor<f32>)
    %2 = "tf.Identity"(%1) {_xla_outside_compilation = "0"} : (tensor<f32>) -> (tensor<f32>)
    %3 = "tf.AddV2"(%1, %2) : (tensor<f32>, tensor<f32>) -> (tensor<f32>)
    tf_device.return %3 : tensor<f32>
  }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> tensor<f32>
  return %0 : tensor<f32>
}
```

will become a tf_device.parallel_execute op with a CPU/host region and
a tf_device.cluster with communication ops to send data to/from device/host:

```mlir
func @outside_compilation() -> tensor<f32> {
  %0 = "tf_device.parallel_execute"() ( {
    "tf_device.launch"() ( {
      %1 = "tf._TPUCompileMlirPlaceholderProgramKey"() : () -> tensor<3x!tf_type.string>
      %2 = "tf._XlaRecvAtHost"(%1) {device_ordinal = 0 : i64, key = "host_compute_channel_0_0_args"} : (tensor<3x!tf_type.string>) -> tensor<f32>
      %3 = "tf.Identity"(%2) : (tensor<f32>) -> tensor<f32>
      "tf._XlaSendFromHost"(%3, %1) {device_ordinal = 0 : i64, key = "host_compute_channel_0_0_retvals"} : (tensor<f32>, tensor<3x!tf_type.string>) -> ()
      tf_device.return
    }) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : () -> ()
    tf_device.return
  },  {
    %1 = "tf_device.cluster"() ( {
      %2 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
      %3 = "tf._XlaHostComputeMlir"(%2) {recv_key = "host_compute_channel_0_0_retvals", send_key = "host_compute_channel_0_0_args", tpu_core = 0 : i64} : (tensor<f32>) -> tensor<f32>
      %4 = "tf.AddV2"(%2, %3) : (tensor<f32>, tensor<f32>) -> tensor<f32>
      tf_device.return %4 : tensor<f32>
    }) {device_assignment = [], num_cores_per_replica = 1 : i64, topology = ""} : () -> tensor<f32>
    tf_device.return %1 : tensor<f32>
  }) : () -> tensor<f32>
  return %0 : tensor<f32>
}
```
### `-tf-tpu-host-computation-expansion`: Expands host computation before and after TPU computation.
This pass expands outside compilation attributes to Identity/Cast ops
at the head of TPU computation if it's only used by outside compiled ops.
### `-tf-tpu-merge-variables-with-execute`: Merges device variable reads and updates into TPU execute ops
This pass finds on-device resource variable reads and updates surrounding a
`tf.TPUExecute` op and merges them into a `tf.TPUExecuteAndUpdateVariables`
op. This allows the TPU execution to perform more efficient in-place
variable updates.

For example,

```mlir
  %0 = "tf.ReadVariableOp"(%arg0)
  %1 = "tf.ReadVariableOp"(%arg1)
  %2 = "tf.TPUExecute"(%0, %1, %compile)
  %3 = "tf.AssignVariableOp"(%arg0, %2)
```

will be transformed into

```mlir
  %2 = "tf.TPUExecuteAndUpdateVariables"(%arg0, %arg1, %compile)
    { device_var_reads_indices = [0, 1],
      device_var_updates_indices = [0, -1] }
````

The transformation happens only for on-device variables. The above
transformation requires `%arg0`, `%arg1` to have the same device assignment
as the `TPUExecute` op.
### `-tf-tpu-reorder-replicate-partitioned-inputs`: Reorder replicated and partitioned input ops.
This pass rewrites how data parallelism and model parallelism is expressed for
inputs. It reorders `tf.TPUPartitionedInput` (model parallelism) and
`tf.TPUReplicatedInput` (data parallelism) ops. It transforms a DAG where
multiple `tf.TPUPartitionedInput` ops are feeding into a single
`tf.TPUReplicatedInput` into a DAG where multiple `tf.TPUReplicatedInput` ops
are feeding into a single `tf.TPUPartitionedInput`. Transforming the IR in such
a manner will allow subsequent cluster formation pass to handle IR with both
data and model parallelism in an easier manner.

For example, the following:

```mlir
!rtype = type tensor<!tf_type.resource<tensor<10x3xf32>>>
func @data_and_model_parallelism(%arg0: !rtype, %arg1: !rtype, %arg2: !rtype, %arg3: !rtype) -> !rtype {
  %pi_0 = "tf.TPUPartitionedInput"(%arg0, %arg1) {_XlaSharding = "", device = "", partition_dim = -1 : i64} : (!rtype, !rtype) -> !rtype
  %pi_1 = "tf.TPUPartitionedInput"(%arg2, %arg3) {_XlaSharding = "", device = "", partition_dim = -1 : i64} : (!rtype, !rtype) -> !rtype
  %ri = "tf.TPUReplicatedInput"(%pi_0, %pi_1) : (!rtype, !rtype) -> !rtype
  return %ri : !rtype
}
```

will be transformed into:

```mlir
!rtype = type tensor<!tf_type.resource<tensor<10x3xf32>>>
func @data_and_model_parallelism(%arg0: !rtype, %arg1: !rtype, %arg2: !rtype, %arg3: !rtype) -> !rtype {
  %ri_0 = "tf.TPUReplicatedInput"(%arg0, %arg2) : (!rtype, !rtype) -> !rtype
  %ri_1 = "tf.TPUReplicatedInput"(%arg1, %arg3) : (!rtype, !rtype) -> !rtype
  %pi = "tf.TPUPartitionedInput"(%ri_0, %ri_1) {_XlaSharding = "", device = "", partition_dim = -1 : i64} : (!rtype, !rtype) -> !rtype
  return %pi : !rtype
}
```
### `-tf-tpu-resource-partition`: Partitions unpartitioned resource read/write to partitioned resource variables.
This pass creates individual resource reads/writes from the unpartitioned
resource variable (from `tf.TPUPartitionedInput`) to individual partitioned
resource variables (`tf.TPUPartitionedInput` operands). As resource op
decomposition/lifting occurs with the unpartitioned resource variables,
transforming the IR in such a manner will allow for subsequent passes to operate
on individual resource variable handles per core/device.

For example, the following:

```mlir
func @cluster(%arg0: tensor<!tf_type.resource<tensor<i32>>>, %arg1: tensor<!tf_type.resource<tensor<i32>>>) {
  %partitioned_variable = "tf.TPUPartitionedInput"(%arg0, %arg1) {N = 2 : i64, _XlaSharding = "", partition_dim = -1 : i64} : (tensor<!tf_type.resource<tensor<i32>>>, tensor<!tf_type.resource<tensor<i32>>>) -> tensor<!tf_type.resource<tensor<i32>>>
  %read = "tf.ReadVariableOp"(%partitioned_variable) : (tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %computation = "tf_device.cluster_func"(%read) {func = @computation, use_spmd_for_xla_partitioning = true} : (tensor<i32>) -> tensor<i32>
  "tf.AssignVariableOp"(%partitioned_variable, %computation) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  return
}

func @computation(%arg0: tensor<i32>) -> tensor<i32> {
  return %arg0: tensor<i32>
}
```

will be transformed into:

```mlir
func @cluster(%arg0: tensor<!tf_type.resource<tensor<i32>>>, %arg1: tensor<!tf_type.resource<tensor<i32>>>) {
  %read0 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %read1 = "tf.ReadVariableOp"(%arg1) : (tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %partitioned_input = "tf.TPUPartitionedInput"(%read0, %read1) {N = 2 : i64, _XlaSharding = "", partition_dim = -1 : i64} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %computation = "tf_device.cluster_func"(%partitioned_input) {func = @computation, use_spmd_for_xla_partitioning = true} : (tensor<i32>) -> tensor<i32>
  %partitioned_output:2 = "tf.TPUPartitionedOutput"(%computation) {N = 2 : i64, _XlaSharding = "", partition_dim = -1 : i64} : (tensor<i32>) -> (tensor<i32>, tensor<i32>)
  "tf.AssignVariableOp"(%arg0, %partitioned_output#0) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  "tf.AssignVariableOp"(%arg1, %partitioned_output#1) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  return
}

func @computation(%arg0: tensor<i32>) -> tensor<i32> {
  return %arg0: tensor<i32>
}
```
### `-tf-tpu-resource-read-for-write`: Inserts tf.ReadVariableOp inputs to a TPU cluster for resource writes with no reads
This pass materializes `tf.ReadVariableOp` inputs to an outlined TPU computation
for resource variables where only writes are present so later in the pipeline
such resource variables can be fused with generated `tf.TPUExecute` ops, which
only supports resource variable read or read + write. For all TPU computations,
resource variables are required to be initialized prior to execution. Write only
resource variable uses can be generated currently via packed tensor uses.

For example, the following:

```mlir
func @write_only_resource(%value: tensor<i32>, %resource: tensor<*x!tf_type.resource<tensor<i32>>>) {
  %0 = "tf_device.cluster_func"(%value) {func = @cluster} : (tensor<i32>) -> tensor<i32>
  "tf.AssignVariableOp"(%resource, %0) : (tensor<*x!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  return
}

func @cluster(%arg0: tensor<i32>) -> tensor<i32> {
  %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
  return %identity : tensor<i32>
}
```

will be transformed into:

```mlir
func @write_only_resource(%value: tensor<i32>, %resource: tensor<*x!tf_type.resource<tensor<i32>>>) {
  %resource_read = "tf.ReadVariableOp"(%resource) : (tensor<*x!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %0 = "tf_device.cluster_func"(%value, %resource_read) {func = @cluster} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  "tf.AssignVariableOp"(%resource, %0) : (tensor<*x!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  return
}

func @cluster(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
  %identity = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>
  return %identity : tensor<i32>
}
```
### `-tf-tpu-rewrite`: Rewrites a `tf_device.cluster_func` on TPUs into TPU runtime operations.
This pass rewrites a `tf_device.cluster_func` operation into a sequence of `tf._TPUCompileMlir`
and `tf.TPUExecute` operations. `tf._TPUCompileMlir` contains a MLIR module that is
functionally equivalent to the function referenced by `tf_device.cluster_func`.
This makes the module to be jit-compiled and executed on TPU.
If it is not possible to rewrite the operation or device assignment fails,
a failure will be returned.

Note, many parameters to the `tf_device.cluster_func` are ommited in this
and following examples.
For example, a non replicated `tf_device.cluster_func`:

```mlir
func @tf_tpu_rewrite(%arg0: tensor<i8>) {
  %0 = "tf_device.cluster_func"(%arg0) {_tpu_replicate = "cluster0", func = @func} : (tensor<i8>) -> tensor<i8>
  return
}
```

will be rewritten as:

```mlir
func @tf_tpu_rewrite(%arg0: tensor<i8>) {
  %0:2 = "tf_device.launch"() ( {
    %compilation_status, %program = "tf._TPUCompileMlir"() {mlir_module = "<serialized func>"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>)
    tf_device.return %compilation_status, %program : tensor<!tf_type.string>, tensor<3x!tf_type.string>
  }) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>)
  "tf_device.launch"() ( {
    "tf.TPUCompileSucceededAssert"(%0#0) : (tensor<!tf_type.string>) -> ()
    tf_device.return
  }) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : () -> ()
  %1 = "tf_device.launch"() ( {
    %2 = "tf.TPUExecute"(%arg0, %0#1) : (tensor<i8>, tensor<3x!tf_type.string>) -> tensor<i8>
    tf_device.return %2 : tensor<i8>
  }) {device = "/job:worker/replica:0/task:0/device:TPU:0"} : () -> tensor<i8>
  return
}
```

A replicated `tf_device.cluster_func`:

```mlir
func @tf_tpu_rewrite(%arg0: tensor<i8>, %arg1: tensor<i8>) {
  %0:2 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<i8>) {n = 2 : i32} {
    %1 = "tf_device.cluster_func"(%ri) {_tpu_replicate = "cluster0", func = @func} : (tensor<i8>) -> tensor<i8>
    tf_device.return %1 : tensor<i8>
  }
  return
}
```

will be rewritten as:

```mlir
func @tf_tpu_rewrite(%arg0: tensor<i8>, %arg1: tensor<i8>) {
  %0:2 = tf_device.replicate([%arg0, %arg1] as %arg2: tensor<i8>) {devices = {TPU_REPLICATED_CORE_0 = ["/job:worker/replica:0/task:0/device:TPU:0", "/job:worker/replica:0/task:0/device:TPU:1"], TPU_REPLICATED_HOST = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:CPU:0"]}, n = 2 : i32} {
    %1:2 = "tf_device.launch"() ( {
      %compilation_status, %program = "tf._TPUCompileMlir"() {mlir_module = "<serialized func>"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>)
      tf_device.return %compilation_status, %program : tensor<!tf_type.string>, tensor<3x!tf_type.string>
    }) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>)
    "tf_device.launch"() ( {
      "tf.TPUCompileSucceededAssert"(%1#0) : (tensor<!tf_type.string>) -> ()
      tf_device.return
    }) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : () -> ()
    %2 = "tf_device.launch"() ( {
      %3 = "tf.TPUExecute"(%arg2, %1#1) : (tensor<i8>, tensor<3x!tf_type.string>) -> tensor<i8>
      tf_device.return %3 : tensor<i8>
    }) {device = "TPU_REPLICATED_CORE_0"} : () -> tensor<i8>
    tf_device.return %2 : tensor<i8>
  }
  return
}
```

A non replicated `tf_device.cluster_func` with the model parallelism:

```mlir
func @tf_tpu_rewrite(%arg0: tensor<8xi32>) -> tensor<8xi32> {
  %0 = "tf_device.cluster_func"(%arg0) {_tpu_replicate = "cluster0", func = @func, num_cores_per_replica = 2, input_sharding_configuration = ["\08\01\1A\01\01\22\01\00"], output_sharding_configuration = ["\08\01\1A\01\01\22\01\00"]} : (tensor<8xi32>) -> tensor<8xi32>
  return %0 : tensor<8xi32>
}
```

will be rewritten as:

```mlir
func @tf_tpu_rewrite(%arg0: tensor<8xi32>) -> tensor<8xi32> {
  %0:3 = "tf_device.launch"() ( {
    %compilation_status, %program:2 = "tf._TPUCompileMlir"() {mlir_module = "<serialized func>"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>, tensor<3x!tf_type.string>)
    tf_device.return %compilation_status, %program#0, %program#1 : tensor<!tf_type.string>, tensor<3x!tf_type.string>, tensor<3x!tf_type.string>
  }) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>, tensor<3x!tf_type.string>)
  "tf_device.launch"() ( {
    "tf.TPUCompileSucceededAssert"(%0#0) : (tensor<!tf_type.string>) -> ()
    tf_device.return
  }) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> ()
  %1 = "tf_device.parallel_execute"() ( {
    %2 = "tf_device.launch"() ( {
      %3 = "tf.TPUExecute"(%arg0, %0#1) : (tensor<8xi32>, tensor<3x!tf_type.string>) -> tensor<8xi32>
      tf_device.return %3 : tensor<8xi32>
    }) {device = "/job:localhost/replica:0/task:0/device:TPU:0"} : () -> tensor<8xi32>
    tf_device.return %2 : tensor<8xi32>
  },  {
    "tf_device.launch"() ( {
      "tf.TPUExecute"(%0#2) : (tensor<3x!tf_type.string>) -> ()
      tf_device.return
    }) {device = "/job:localhost/replica:0/task:0/device:TPU:1"} : () -> ()
    tf_device.return
  }) : () -> tensor<8xi32>
  return %1 : tensor<8xi32>
}
```
### `-tf-tpu-space-to-depth-pass`: Applies automatic space to depth transform for the first or frontier convolutions consume host inputs on TPU.
Automatic space to depth transform is done by adding space to depth transform op after host input
and applying space to depth transform for the first convolution and its backprop filter on TPU.

For example, original program:

```mlir
module {
  func @while_body {
    %input = "tf.IteratorGetNext"(...) {device = "/CPU:0"}: -> tensor<2x224x224x3xf32>
    %device_launch = "tf_device.cluster_func"(%input,...) {func = @_func,...)
    return ...
  }
  func @_func(%input: tensor<2x224x224x3xf32>, %filter: tensor<7x7x3x64xf32>) {
    %6 = "tf.Conv2D"(%input, %filter)  {strides = [1, 2, 2, 1]}: (tensor<2x230x230x3xf32>, tensor<7x7x3x64xf32>) -> tensor<2x112x112x64xf32>
  }
}
```

The program will be transformed into:

```mlir
module {
  func @while_body {
    %input = "tf.IteratorGetNext"(...) {device = "/CPU:0"} -> tensor<2x224x224x3xf32>
    %space_to_depth = "tf.SpaceToDepth"(%input) {block_size = 2, ...}: (tensor<2x224x224x3xf32>) -> tensor<2x112x112x12xf32>
    %device_launch = "tf_device.cluster_func"(%space_to_depth,...) {func = @_func,...)
    return ...
  }
  func @_func(%input: tensor<2x112x112x12xf32>, %filter: tensor<7x7x3x64xf32>) {
    %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter): tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32>
    %conv = "tf.Conv2D"(%input, %filter_transfrom) {strides = [1, 1, 1, 1]}: (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32>
  }
}
```

This way, the first convolution with 3 feature dimension will be transformed
to 12 feature dimension, which has better performance on TPU.
### `-tf-tpu-update-embedding-enqueue-op-inputs`: Updates inputs to TPU embedding enqueue ops depending on whether graph is in training mode or in evaluation mode.
Updates inputs to TPU embedding enqueue ops depending on whether graph
is in training mode or in evaluation mode.
### `-tf-verify-for-export`: Verify module is suitable for export back to TF Graph
Verifies whether all functions in module are of single tf_executor.graph and
each tf_executor.island in tf_executor.graph only has a single op.
