// Return a tensor handle containing 2D matrix containing given data and
// dimensions
TFE_TensorHandle* TestMatrixTensorHandleWithInput(TFE_Context* ctx,
                                                  float data[], int64_t dims[],
                                                  int num_dims);

// Get a Matrix TensorHandle with given float values and dimensions
TFE_TensorHandle* TestTensorHandleWithDimsFloat(TFE_Context* ctx, float data[],

class ImmediateExecutionTensorHandle : public AbstractTensorHandle {
 public:
  // Returns number of dimensions.
  virtual absl::Status NumDims(int* num_dims) const = 0;
  // Returns number of elements across all dimensions.
  virtual absl::Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard

\",\"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\"...

// 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.

void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

t:"Si us plau, triï almenys ",groupCheckedTooManyStart:"Si us plau, triï un màxim de ",groupCheckedEnd:" element(s)",badCreditCard:"El número de targeta de crèdit proporcionat no és vàlid",badCVV:"CVV proporcionat no és vàlid",wrongFileDim:"Les dimensions de la imatge no són vàlides,",imageTooTall:"l'alçada de la imatge no pot ser major a",imageTooWide:"l'amplada de la imatge no pot ser major a",imageTooSmall:"la imatge és massa petita",min:"min.",max:"màx.",imageRatioNotAccepted:"La proporció de...

    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;

// entries (e.g., the return value of TF_GraphGetTensorNumDims).
//
// If the number of dimensions in the shape is unknown or the shape is
// a scalar, `dims` will remain untouched. Otherwise, each element of
// `dims` will be set corresponding to the size of the dimension. An
// unknown dimension is represented by `-1`.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.

c.imageRatioNotAccepted;return!1},a.formUtils.addAsyncValidator({name:"dimension",validatorFunction:function(b,d,e,g,h){if(c){var i=e.get(0).files||[],j=this;e.attr("data-validation").indexOf("mime")===-1?(alert("You should validate file type being jpg, gif or png on input "+e[0].name),b(!1)):i.length>1?(alert("Validating image dimensions does not support inputs allowing multiple files"),b(!1)):0===i.length?b(!0):f(i[0],function(c){var d=!1;e.valAttr("dimension")&&(d=a.formUtils.checkImageDimension(c,e.valAttr("d...

  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("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 {