// RUN: tf-opt -split-input-file -verify-diagnostics -tf-unroll-batch-matmul %s | FileCheck %s

//==== V1 tests ====

func.func @batchMatMulTwoDim(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) : (tensor<2x3x4x5xf32>, tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32>
  func.return %0 : tensor<2x3x4x6xf32>

  // CHECK-LABEL: batchMatMulTwoDim

import org.gradle.integtests.fixtures.GradleMetadataResolveRunner
import org.gradle.integtests.fixtures.RequiredFeature
import org.gradle.integtests.resolve.AbstractModuleDependencyResolveTest
import spock.lang.Issue
import spock.lang.Unroll

class DependenciesAttributesIntegrationTest extends AbstractModuleDependencyResolveTest {

    def setup() {
        buildFile << """
            def CUSTOM_ATTRIBUTE = Attribute.of('custom', String)

		// Handle the case where we have no malloc header.
		scanSize = span.writeHeapBitsSmall(x, dataSize, typ)
	} else {
		if typ.Kind_&abi.KindGCProg != 0 {
			// Allocate space to unroll the gcprog. This space will consist of
			// a dummy _type value and the unrolled gcprog. The dummy _type will
			// refer to the bitmap, and the mspan will refer to the dummy _type.
			if span.spanclass.sizeclass() != 0 {
				throw("GCProg for type that isn't large")

  tf_device.replicate ([%var0, %var1] as %rvar,
                       [%state_var0, %state_var1] as %rstate) {
    tf.TPUReshardVariablesOp(%rvar, %default_format, %rstate)
  }
```
### `-tf-unroll-batch-matmul`

_Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops._

### `-tf-verify-for-export`

_Verify module is suitable for export back to TF Graph_

            if (logger.isDebugEnabled()) {
                logger.debug("Failed to process a scroll request.", e);
            }
            writeJsonResponse(HttpServletResponse.SC_BAD_REQUEST, e);
        } catch (final Exception e) {
            if (logger.isDebugEnabled()) {
                logger.debug("Failed to process a scroll request.", e);
            }

        try (CurlResponse response = ComponentUtil.getCurlHelper().post("/_reindex").param("refresh", refresh)
                .param("requests_per_second", requestsPerSecond).param("scroll", scroll).param("max_docs", maxDocs)
                .param("wait_for_completion", Boolean.toString(waitForCompletion)).body(source).execute()) {
            if (response.getHttpStatusCode() == 200) {
                return true;
            }

  %shift = "tf.Const"() <{value = dense<2> : tensor<i32>}> : () -> tensor<i32>
  %0 = "tf.Roll"(%arg0, %shift, %axis) : (tensor<?x8x4xi32>, tensor<i32>, tensor<i32>) -> tensor<?x8x4xi32>
  func.return %0 : tensor<?x8x4xi32>
  // CHECK-LABEL: roll_dynamic_shape
  // CHECK:  "tf.Roll"
}

func.func @roll_non_constant_axis(%arg0: tensor<3x8x4xi32>, %arg1: tensor<i32>) -> tensor<3x8x4xi32> {

						continue
					}

					if err = Rename(pathJoin(currentDataPath, entry), pathJoin(legacyDataPath, entry)); err != nil {
						// Any failed rename calls un-roll previous transaction.
						s.deleteFile(dstVolumeDir, legacyDataPath, true, false)

						return res, osErrToFileErr(err)
					}
				}
			}
		}
	}

                  “roll up” the /32s into a larger prefix. Defaults to 32. Works for
                  IPv4 addresses.
                format: int32
                minimum: 1
                type: integer
              aggregationLengthV6:
                default: 128
                description: The aggregation-length advertisement option lets you

an"!=typeof e),h=c||(e===!0||f===!0?"margin":"border");return T(this,function(b,c,e){var f;return r.isWindow(b)?0===d.indexOf("outer")?b["inner"+a]:b.document.documentElement["client"+a]:9===b.nodeType?(f=b.documentElement,Math.max(b.body["scroll"+a],f["scroll"+a],b.body["offset"+a],f["offset"+a],f["client"+a])):void 0===e?r.css(b,c,h):r.style(b,c,e,h)},b,g?e:void 0,g)}})}),r.fn.extend({bind:function(a,b,c){return this.on(a,null,b,c)},unbind:function(a,b){return this.off(a,null,b)},delegate:func...