* License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 *
 * This library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *

  integrity sha512-On2N+BpYJ15xIC974QNVuYGMOlEVt4s0EOI3wwMqOmK1fdDY+FN/zltPV8vosq4ad4c/gJ1KHScUn/6AWIgiow==

fill-range@^7.0.1:
  version "7.0.1"
  resolved "https://registry.yarnpkg.com/fill-range/-/fill-range-7.0.1.tgz#1919a6a7c75fe38b2c7c77e5198535da9acdda40"
  integrity sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==
  dependencies:

				}
			}
			if loggingEnabled {
				state.logf("\n")
			}
		}
		// fill in length of location expression element
		ctxt.Arch.ByteOrder.PutUint16(list[sizeIdx:], uint16(len(list)-sizeIdx-2))

		// Second entry in the location list will be the stack home
		// of the param, once it has been spilled.  Emit that now.
		list, sizeIdx = setupLocList(ctxt, f, list,

      %35 = "tfl.shape"(%34) {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<?x!tf_type.string>) -> tensor<1xi32>
      %36 = "tfl.fill"(%35, %cst_7) {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<1xi32>, tensor<!tf_type.string>) -> tensor<?x!tf_type.string>

  // CHECK-LABEL: UnsupportedGroupConv_DynamicDimAtInputDimThree
  // CHECK-NOT: "tfl.conv_2d"
  // CHECK: "tf.Conv2D"
}

func.func @RedundantShapeOp(%shape: tensor<?xi64>, %fill: tensor<f32>) -> (tensor<?xi64>) {
  %0 = "tf.Fill"(%shape, %fill) : (tensor<?xi64>, tensor<f32>) -> (tensor<*xf32>)
  %1 = "tf.Shape"(%0) : (tensor<*xf32>) -> (tensor<?xi64>)
  func.return %1 : tensor<?xi64>

  // CHECK-LABEL: RedundantShapeOp

      <svg xmlns="http://www.w3.org/2000/svg" width="768" height="768">\
        <path d="M584 664H104V184h216V80H0v688h688V448H584zM384 0l132 \
        132-240 240 120 120 240-240 132 132V0z" fill="%234a6782"/>\
      </svg>');
    background-size:100% 100%;
    width:7px;
    height:7px;
    margin-left:2px;
    margin-bottom:4px;
}
main a[href*="://"]:hover::after,

     *   where multiple modules in the reactor have the same artifactId.
     * <p>
     * {@code -rf :artifactId} will pick up the first module which matches, but when multiple modules in the reactor
     *   have the same artifactId, effective failed module might be later in build reactor.
     * This means that developer will either have to type groupId or wait for build execution of all modules which

    TF_CHECK_OK(AddOpIntermediatesForLstm(op, intermediate_types, op_state, loc,
                                          builder));
  }
  if (op_name == "tfl.while") {
    // Adds two empty regions for "tfl.while". We will fill the regions after
    // creating the callee functions because the "tfl.while" input/output types
    // may be different with the callee functions, and the call ops need to sync
    // with callee function types.

/// pointed to by the `void*` members is always owned by the plugin. The plugin
/// will provide functions to call to allocate and deallocate this data (see
/// next sections) and core TensorFlow ensures to call these at the proper time.
///
/// Plugins will never receive a `TF_*` pointer that is `nullptr`. Core
/// TensorFlow will never touch the `void*` wrapped by these structures, except
/// to initialize it as `nullptr`.

 protected:
  // This flag will control the behavior of error emitting during rewrite:
  // 1) If it's true, then patterns will only emit errors during debug or
  // tracing mode. 2) If it's false, then patterns will emit standard errors
  // when there is a rewrite failure.
  bool allow_tensorlist_pass_through_;

  // This flag will control the behavior of setting the batch size one when the