\",\"data\":{\"label\":\"Query Count\",\"id\":\"1\"},\"valueAxis\":\"ValueAxis-1\",\"drawLinesBetweenPoints\":true,\"showCircles\":true}],\"addTooltip\":true,\"addLegend\":true,\"legendPosition\":\"right\",\"times\":[],\"addTimeMarker\":false,\"dimensions\":{\"x\":{\"accessor\":0,\"format\":{\"id\":\"date\",\"params\":{\"pattern\":\"HH:mm\"}},\"params\":{\"date\":true,\"interval\":\"PT10M\",\"format\":\"HH:mm\",\"bounds\":{\"min\":\"2019-07-28T10:40:08.271Z\",\"max\":\"2019-07-30T10:40:08.271Z\"...

/**
 * HTML tag-based thumbnail generator that creates thumbnails from image content
 * referenced in HTML documents. This generator extracts images from HTML content
 * and processes them to create thumbnail images based on configured dimensions
 * and format settings.
 *
 * <p>The generator validates image MIME types, processes image data through
 * ImageIO operations, and applies scaling and cropping to generate thumbnails

// Returns the number of elements in dimension `dim_index`.
// Tensor representation on device can be transposed from its representation
// on host. The data contained in dimension `dim_index` on device
// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow
// major-to-minor order (slowest varying dimension first),
// not the XLA's minor-to-major order.

    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

  if (num_dims > TensorShape::MaxDimensions()) {
    return absl::InvalidArgumentError(
        absl::StrCat("Value specified for `", attr_name, "` has ", num_dims,
                     " dimensions which is over the limit of ",
                     TensorShape::MaxDimensions(), "."));
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

                    /*input_shapes*/ {make_shape({3, 2}), unknown_shape()},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, kUnknownDim}));

  // Infer shape when some dimensions are unknown.
  CheckOutputShapes(
      matmul_op,
      /*input_shapes*/ {make_shape({kUnknownDim, 2}), make_shape({2, 4})},
      /*input_tensors*/ {},
      /*expected_shape*/ make_shape({kUnknownDim, 4}));

  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {
      valid = false;
    }
    expected_stride *= shape_arr[i];
  }
  return valid;
}
}  // namespace

  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];
  memcpy(shape.dims, dims, sizeof(int64_t) * num_dims);
}

",groupCheckedTooFewStart:"Please choose at least ",groupCheckedTooManyStart:"Please choose a maximum of ",groupCheckedEnd:" item(s)",badCreditCard:"The credit card number is not correct",badCVV:"The CVV number was not correct",wrongFileDim:"Incorrect image dimensions,",imageTooTall:"the image can not be taller than",imageTooWide:"the image can not be wider than",imageTooSmall:"the image was too small",min:"min",max:"max",imageRatioNotAccepted:"Image ratio is not be accepted",badBrazilTelephoneAnswer:"The phone...

// are cached and refreshed after a given timeout.
//
// For metrics with a `bucket` dimension, a list of buckets argument is required
// to collect the metrics.
//
// It implements the prometheus.Collector interface for metric groups without a
// bucket dimension. For metric groups with a bucket dimension, use the
// `GetBucketCollector` method to get a `BucketCollector` that implements the