!this._isEnabled\n  }\n\n  toggle(event) {\n    if (!this._isEnabled) {\n      return\n    }\n\n    if (event) {\n      const dataKey = this.constructor.DATA_KEY\n      let context = $(event.currentTarget).data(dataKey)\n\n      if (!context) {\n        context = new this.constructor(\n          event.currentTarget,\n          this._getDelegateConfig()\n        )\n        $(event.currentTarget).data(dataKey, context)\n      }\n\n      context._activeTrigger.click = !context._activeTrigger.click\n\n      if (conte...

!this._isEnabled\n  }\n\n  toggle(event) {\n    if (!this._isEnabled) {\n      return\n    }\n\n    if (event) {\n      const dataKey = this.constructor.DATA_KEY\n      let context = $(event.currentTarget).data(dataKey)\n\n      if (!context) {\n        context = new this.constructor(\n          event.currentTarget,\n          this._getDelegateConfig()\n        )\n        $(event.currentTarget).data(dataKey, context)\n      }\n\n      context._activeTrigger.click = !context._activeTrigger.click\n\n      if (conte...

				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),
				testCase.accessKey, testCase.secretKey)
		case testCase.contentEncoding == "":
			req, err = newTestStreamingSignedRequest(http.MethodPut,
				getPutObjectURL("", testCase.bucketName, testCase.objectName),
				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),

    self.assertTrue(
        file_io.file_exists_v2(
            os.path.join(self._output_saved_model_path, asset_filename)
        )
    )

  # tf.data.Dataset is as an Iterable (thus can be used as representative
  # dataset) only in TF2 (eager mode).
  @test_util.run_v2_only
  def test_model_ptq_use_tf_dataset_for_representative_dataset(self):
    self._create_matmul_model(
        input_shape=(1, 1024),

    RankedTensorType index_ty =
        mlir::dyn_cast<RankedTensorType>(index.getType());
    RankedTensorType data_ty = mlir::dyn_cast<RankedTensorType>(data.getType());
    if (!index_ty || !data_ty) continue;

    int64_t index_rank = index_ty.getRank();
    ArrayRef<int64_t> data_shape = data_ty.getShape();
    ArrayRef<int64_t> index_shape = index_ty.getShape();
    if (failed(mlir::verifyCompatibleShape(index_shape,

  }
  return false;
}

// Helper structure to capture shapes & types for Dataset input.
struct DatasetInput {
  explicit operator bool() const { return shapes && types; }

  ArrayAttr shapes;
  ArrayAttr types;
};

// Returns the input elements shapes and types for Dataset ops.
DatasetInput GetDatasetInput(Value value) {

      }
    ```
  }];
}

def DecomposeReduceDatasetPass : Pass<"tf-decompose-reduce-dataset", "mlir::func::FuncOp"> {
  let summary = "Decomposes ReduceDataset op into dataset operations.";

  let description = [{
    Decomposes ReduceDataset op into a while loop that iterates the dataset and calls
    into the reduction function.  This decomposition is only done if the

      "properties": {
        "datasetName": {
          "description": "Name of the dataset stored as metadata -> name on the dataset for Flocker should be considered as deprecated",
          "type": "string"
        },
        "datasetUUID": {
          "description": "UUID of the dataset. This is unique identifier of a Flocker dataset",
          "type": "string"
        }
      },
      "type": "object"
    },

lean,offsetBottom:Boolean,minHeight:Number},data:{expand:!1,offsetTop:!1,offsetBottom:!1,minHeight:0},update:{read:function(t){var e=t.minHeight;if(!Wt(this.$el))return!1;var i="",n=fi(this.$el,"height","content-box");if(this.expand){if(this.$el.dataset.heightExpand="",Ce("[data-height-expand]")!==this.$el)return!1;i=ci(window)-(Cn(document.documentElement)-Cn(this.$el))-n||""}else{if(i="calc(100vh",this.offsetTop){var r=si(this.$el).top;i+=0<r&&r<ci(window)/2?" - "+r+"px":""}!0===this.offsetBottom?i+="...

      //   [arg0, arg5]:   these are states coming from ReduceDatasetOp's [arg1, arg6]
      //   [arg6: arg14]:  these are the input elements coming from arg1 variant with undefined dataset outputs.
      //   [arg15: arg35]: these are the captured elements from ReduceDatasetOp's [arg7:arg27]