// Allocate output
    TF_Status* s = TF_NewStatus();
    int64_t dim = 1;
    size_t tensor_size_bytes = TF_DataTypeSize(TF_FLOAT);
    TF_Tensor* output = TF_AllocateOutput(
        /*context=*/ctx, /*index=*/0, /*dtype=*/TF_FLOAT, /*dims=*/&dim,
        /*num_dims=*/1, /*len=*/tensor_size_bytes, s);
    validate_tensor(output, &dim, 1, TF_FLOAT);

    // Set output to 3
    float values[1] = {3.0f};

      %1 = stablehlo.get_dimension_size %0, dim = 0 : (tensor<?x3xf32>) -> tensor<i32>
      %2 = stablehlo.reshape %1 : (tensor<i32>) -> tensor<1xi32>
      %3 = stablehlo.concatenate %2, %cst_0, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
      %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [1] : (tensor<3xf32>, tensor<2xi32>) -> tensor<?x3xf32>

  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);
  delete[] dims;

  %5 = stablehlo.dynamic_broadcast_in_dim %1, %4, dims = [3] : (tensor<16xf32>, tensor<4xindex>) -> tensor<?x28x28x16xf32>
  %6 = stablehlo.add %3, %5 : tensor<?x28x28x16xf32>
  %7 = shape.shape_of %6 : tensor<?x28x28x16xf32> -> tensor<4xindex>
  %8 = stablehlo.dynamic_broadcast_in_dim %2, %7, dims = [] : (tensor<f32>, tensor<4xindex>) -> tensor<?x28x28x16xf32>

      merge_fusion_with_dequantize: bool,
  ):
    lhs_dim_size, rhs_dim_size = dim_sizes
    input_shape = (*lhs_dim_size,)
    filter_shape = (*rhs_dim_size,)
    static_input_shape = [dim if dim is not None else 2 for dim in input_shape]
    model = self._create_matmul_model(
        input_shape,
        filter_shape,
        self._input_saved_model_path,
        bias_fn,
        activation_fn,
    )

// Returns the shape of the Tensor referenced by `output` in `graph`
// into `dims`. `dims` must be an array large enough to hold `num_dims`
// 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

        given:
        file('dir1/path/file.txt').createFile() << 'f1'
        file('dir2/path/file.txt').createFile() << 'f2'
        buildScript '''
            task copy(type: Copy) {
                from 'dir1'
                from 'dir2'
                into 'dest'
            }
        '''.stripIndent()

        when:
        fails 'copy'

  %0 = "tf.Squeeze"(%arg0) {T = f32, _output_shapes = ["tfshape$dim { size: 4 } dim { size: 64 } dim { size: 64 }"], device = "", squeeze_dims = []} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>

  TFE_DeleteContextOptions(opts);

  std::vector<int64_t> dims(4, 1);
  TFE_Op* op = TFE_NewOp(ctx, "AvgPool", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* tensor =
      TF_AllocateTensor(TF_FLOAT, dims.data(), dims.size(), sizeof(float));
  float tensor_data[] = {1};

	if len(st.str) == 0 {
		st.fail("missing array dimension")
	}

	var dim AST
	if st.str[0] == '_' {
		dim = &Name{Name: ""}
	} else if isDigit(st.str[0]) {
		i := 1
		for len(st.str) > i && isDigit(st.str[i]) {
			i++
		}
		dim = &Name{Name: st.str[:i]}
		st.advance(i)
	} else {
		dim = st.expression()
	}

	if len(st.str) == 0 || st.str[0] != '_' {