handle git

env GIT_AUTHOR_NAME='Jay Conrod'
env GIT_AUTHOR_EMAIL='******@****.***'
env GIT_COMMITTER_NAME=$GIT_AUTHOR_NAME
env GIT_COMMITTER_EMAIL=$GIT_AUTHOR_EMAIL

at 2019-05-09T18:35:00-04:00

git init

git add sub
git commit -m 'create module sub'
git branch -m master

echo 'v0.1.0'
cp stdout status
git add status
git commit -a -m 'v0.1.0'
git tag 'v0.1.0'

echo 'sub/v0.0.9'
cp stdout status

// Checks if the value has only one user.
def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;

// If we see a Conv2D op followed by Mul, then multiply the filter
// with the value in Mul.
def FuseMulAndConv2D :
  Pat<(TF_MulOp:$mul (TF_Conv2DOp:$conv $input,
                          (Arith_ConstantOp:$filter F32ElementsAttr:$filter_value),

Michael Levesque-Dion <******@****.***> 1706075999 -0800

In this pass, every op will be targeted with the user specified targets based on
the device capabilites. For example, If the user specified the desired targets
are "GPU", "CPU", `conv2d` can run on both "GPU" and "CPU", we will annotate
the op `conv2d` with "GPU" since it's preferred; `pack` can only run on "CPU",
so we will annotate the op with "CPU" since "GPU" does not support this op.

#### Raise Target Subgraphs Pass

    func.return %arg0 : tensor<*xi32>
  }

  // Test conv2d inferReturnTypes can infer some information when input or
  // filter does not have fully static shape.

  // CHECK-LABEL: func @conv2d_unranked_input_and_filter
  func.func @conv2d_unranked_input_and_filter(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: -> tensor<?x?x?x?xf32>

                           op.input(2), strides, padding, filter_attrs));

  Conv2D::Attrs conv_attrs;
  conv_attrs.use_cudnn_on_gpu_ = use_cudnn_on_gpu;
  conv_attrs.explicit_paddings_ = explicit_paddings;
  conv_attrs.data_format_ = data_format;
  conv_attrs.dilations_ = dilations;
  grad_outputs->push_back(
      Conv2D(scope, grad_inputs[0], op.input(1), strides, padding, conv_attrs));
  return scope.status();
}

         * See {@code org.gradle.api.internal.tasks.TaskStateInternal#isActionable()}.
         */
        boolean isActionable();

        /**
         * If task was UP_TO_DATE or FROM_CACHE, this will convey the ID of the build that produced the outputs being reused.
         * Value will be null for any other outcome.
         *

    %5 = "tf.Pad"(%arg0, %3) : (tensor<2x224x224x3xf32>, tensor<4x2xi32>) -> tensor<2x230x230x3xf32>
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: strides = [1, 1, 1, 1]
    // CHECK-SAME: (tensor<2x115x115x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32>

 limitations under the License.
-->
Apache Maven XML Impl
=========================

  %0 = "tf.Conv2D"(%output, %cst) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true}> {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", device...