output.text = String.valueOf(input.length())
                }
            }
        """
    }

    def "applies transforms to artifacts for external dependencies matching on implicit format attribute"() {
        def m1 = mavenRepo.module("test", "test", "1.3").publish()
        m1.artifactFile.text = "1234"
        def m2 = mavenRepo.module("test", "test2", "2.3").publish()

}

// PackageOpts control the behavior of PackagesAndErrors and other package
// loading functions.
type PackageOpts struct {
	// IgnoreImports controls whether we ignore explicit and implicit imports
	// when loading packages.  Implicit imports are added when supporting Cgo
	// or SWIG and when linking main packages.
	IgnoreImports bool

	// ModResolveTests indicates whether calls to the module loader should also

  let description = [{
    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

	    <a href="#For_statements">"for"</a>, and
	    <a href="#Switch_statements">"switch"</a>
	    statement is considered to be in its own implicit block.</li>

	<li>Each clause in a <a href="#Switch_statements">"switch"</a>
	    or <a href="#Select_statements">"select"</a> statement
	    acts as an implicit block.</li>
</ol>

<p>
Blocks nest and influence <a href="#Declarations_and_scope">scoping</a>.
</p>

func.func @testConcatCwiseBinaryInvalidInnerDim(%arg0: tensor<?x2xf32>,
  %arg1: tensor<?x2xf32>, %arg2: tensor<f32>, %arg3: tensor<f32>) -> tensor<?x4xf32> {
  // Each individual binary operation has an implicit broadcast that will be
  // lost if we would reorder them with the concat.

  // CHECK: %[[CONST:.*]] = "tf.Const"()
  // CHECK-DAG: %[[MUL1:.*]] = "tf.Mul"(%arg0, %arg2)

  %1 = "stablehlo.dot_general"(%arg0, %0) {
    dot_dimension_numbers = #stablehlo.dot<
      lhs_batching_dimensions = [0, 1],
      rhs_batching_dimensions = [0, 1],
      // implicit transpose of lhs
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [2]
    >,
    precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]

// float32 tensors.
// TODO(hongm): Implement this op synthesis optimization for other dtypes if
// needed.
//
// Because coefficient-wise binary operations support implicit broadcasting, we
// should be very careful with this optimization, and do not accidentally
// produce incorrect concat operations.
class HoistCwiseBinaryOutOfConcat : public OpRewritePattern<TF::ConcatV2Op> {
 public:

          "type": "string"
        },

	if rs.lasterr != nil {
		doClose = true
		return false
	}
	return true
}

// Err returns the error, if any, that was encountered during iteration.
// Err may be called after an explicit or implicit [Rows.Close].
func (rs *Rows) Err() error {
	// Return any context error that might've happened during row iteration,
	// but only if we haven't reported the final Next() = false after rows

                            ellipsis_mask,      new_axis_mask, shrink_axis_mask,
                            sparse_begin,       sparse_end,    sparse_strides};

  // If no ellipsis_mask exists then an implicit ellipsis_mask at the end is
  // inserted. This handles cases where foo[2:4] (foo.shape() = [4, 8]) yields
  // a tensor of shape [2, 8], i.e., foo[2:4] is same as foo[2:4, ...].
  if (sparse.ellipsis_mask == 0) {