input: "Case/branch0/_0/mul/y"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Case/branch1/_1/mul_0"
  op: "Mul"
  input: "Case/Case/input_0/_7:1"
  input: "Case/branch1/_1/mul/y"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Case/merge/_9"
  op: "Merge"
  input: "Case/branch0/_0/mul_0"

	<dependencies>
		<dependency name="mymodule2" rev="2.0" e:attr4="value4"/>
		<dependency name="mymodule3" rev="2.0" changing="true" transitive="false"/>
		<dependency org="yourorg" name="yourmodule1" branch="trunk" rev="1.1" branchConstraint="branch1" revConstraint="1+" conf="myconf1"/>
		<dependency org="yourorg" name="yourmodule2" rev="2+" conf="myconf1->yourconf1"/>

          default:
            outputs = branches[nbranches-1](inputs);
            break;
        }
        ```
    Example: %res = mlrt.case %branch_idx [@branch0, @branch1] (%arg0, %arg1) : (i32, i32) -> (i32)
  }];

  let arguments = (ins I32:$branch_index,
                       ConfinedAttr<SymbolRefArrayAttr, [ArrayMinCount<1>]>:$branches,
                       Variadic<AnyType>:$branch_operands);

//          which represents the second branch of replicate group 0 and
//          the first branch of parallel group 0.
// First instance of "tf.OpD" after flattening will have
//          `_parallel_execution_ids = "r0:0,p0:1"`
//          which represents the first branch of replicate group 0 and
//          the second branch of parallel group 0.
// Second instance of "tf.OpD" after flattening will have

        %result = "tf.Case"(%index, %input) {branches = [@branch_0, @branch_1, @branch_2, @branch_3, @branch_4], is_stateless = false} : (tensor<i32>, tensor<i32>) -> tensor<i32>
        tf_executor.yield %result : tensor<i32>
      }
      tf_executor.fetch %output : tensor<i32>

    }
    func.return %0 : tensor<i32>
  }
  func.func @branch_0(%arg0: tensor<i32>) -> tensor<i32> {

name: Upload SIG Build docker containers modified for release branches

on:
  workflow_dispatch:
  push:
    paths:
      - '.github/workflows/sigbuild-docker-branch.yml'
      - 'tensorflow/tools/tf_sig_build_dockerfiles/**'
      - '!tensorflow/tools/tf_sig_build_dockerfiles/README.md'
    branches:
      - "r[1-9].[0-9]+"

permissions:
  contents: read

jobs:
  docker:

at 2022-02-02T14:15:21-05:00
git add pkg go.mod
git commit -a -m 'pkg: add empty package'
git branch -m main
git tag 'v0.1.0'

at 2022-02-02T14:19:44-05:00
git branch 'v1.0.0'
git branch 'v2.0.0'
git checkout 'v1.0.0'
cp v1/pkg/pkg.go pkg/pkg.go
git commit -a -m 'pkg: start developing toward v1.0.0'

at 2022-02-03T10:53:13-05:00
git branch 'v3.0.0-devel'
git checkout 'v3.0.0-devel'
git checkout v0.1.0 pkg/pkg.go

# https://github.com/tensorflow/tensorflow/actions/workflows/release-branch-cherrypick.yml
# and click "Run Workflow." Leave "Use Workflow From" set to "master", then
# input the branch name and paste the cherry-pick commit and click Run. A PR
# will be created.

name: Release Branch Cherrypick
on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks

# Adding commits to the above branches updates both the branch heads and the
# corresponding bookmarks.
# But apparently at some point it did not do so? The original copy of this repo
# had bookmarks pointing to the base of each branch instead of the tip. 🤔

		z++
	} else {
		y = g(z, x, y)
	}
	if y+z == 3 { // ERROR "Branch prediction rule call < exit"
		println("ha ha")
	} else {
		panic("help help help")
	}
	if x != 0 { // ERROR "Branch prediction rule default < ret"
		for i := 0; i < x; i++ { // ERROR "Branch prediction rule stay in loop"
			if x == 4 { // ERROR "Branch prediction rule stay in loop"
				return a
			}