Like purrr::map() or base::lapply(), except inputs and outputs are in the form of
stacked arrays. Consider using the op_vectorized_map() transform instead,
unless you need to apply a function element by element for reduced memory
usage or heterogeneous computation with other control flow primitives.
When xs is an array type, the semantics of op_map() match this
implementation:
op_map <- function(xs, f) {
xs |>
op_unstack() |>
lapply(f) |>
op_stack()
}See also
Other core ops: op_associative_scan() op_cast() op_cond() op_convert_to_numpy() op_convert_to_tensor() op_custom_gradient() op_dtype() op_fori_loop() op_is_tensor() op_scan() op_scatter() op_scatter_update() op_searchsorted() op_shape() op_slice() op_slice_update() op_stop_gradient() op_switch() op_unstack() op_vectorized_map() op_while_loop()
Other ops: op_abs() op_add() op_all() op_any() op_append() op_arange() op_arccos() op_arccosh() op_arcsin() op_arcsinh() op_arctan() op_arctan2() op_arctanh() op_argmax() op_argmin() op_argpartition() op_argsort() op_array() op_associative_scan() op_average() op_average_pool() op_batch_normalization() op_binary_crossentropy() op_bincount() op_broadcast_to() op_cast() op_categorical_crossentropy() op_ceil() op_cholesky() op_clip() op_concatenate() op_cond() op_conj() op_conv() op_conv_transpose() op_convert_to_numpy() op_convert_to_tensor() op_copy() op_correlate() op_cos() op_cosh() op_count_nonzero() op_cross() op_ctc_decode() op_ctc_loss() op_cumprod() op_cumsum() op_custom_gradient() op_depthwise_conv() op_det() op_diag() op_diagonal() op_diff() op_digitize() op_divide() op_divide_no_nan() op_dot() op_dtype() op_eig() op_eigh() op_einsum() op_elu() op_empty() op_equal() op_erf() op_erfinv() op_exp() op_expand_dims() op_expm1() op_extract_sequences() op_eye() op_fft() op_fft2() op_flip() op_floor() op_floor_divide() op_fori_loop() op_full() op_full_like() op_gelu() op_get_item() op_greater() op_greater_equal() op_hard_sigmoid() op_hard_silu() op_hstack() op_identity() op_imag() op_image_affine_transform() op_image_crop() op_image_extract_patches() op_image_hsv_to_rgb() op_image_map_coordinates() op_image_pad() op_image_resize() op_image_rgb_to_grayscale() op_image_rgb_to_hsv() op_in_top_k() op_inv() op_irfft() op_is_tensor() op_isclose() op_isfinite() op_isinf() op_isnan() op_istft() op_leaky_relu() op_less() op_less_equal() op_linspace() op_log() op_log10() op_log1p() op_log2() op_log_sigmoid() op_log_softmax() op_logaddexp() op_logical_and() op_logical_not() op_logical_or() op_logical_xor() op_logspace() op_logsumexp() op_lstsq() op_lu_factor() op_matmul() op_max() op_max_pool() op_maximum() op_mean() op_median() op_meshgrid() op_min() op_minimum() op_mod() op_moments() op_moveaxis() op_multi_hot() op_multiply() op_nan_to_num() op_ndim() op_negative() op_nonzero() op_norm() op_normalize() op_not_equal() op_one_hot() op_ones() op_ones_like() op_outer() op_pad() op_power() op_prod() op_psnr() op_qr() op_quantile() op_ravel() op_real() op_reciprocal() op_relu() op_relu6() op_repeat() op_reshape() op_rfft() op_roll() op_round() op_rsqrt() op_scan() op_scatter() op_scatter_update() op_searchsorted() op_segment_max() op_segment_sum() op_select() op_selu() op_separable_conv() op_shape() op_sigmoid() op_sign() op_silu() op_sin() op_sinh() op_size() op_slice() op_slice_update() op_slogdet() op_softmax() op_softplus() op_softsign() op_solve() op_solve_triangular() op_sort() op_sparse_categorical_crossentropy() op_split() op_sqrt() op_square() op_squeeze() op_stack() op_std() op_stft() op_stop_gradient() op_subtract() op_sum() op_svd() op_swapaxes() op_switch() op_take() op_take_along_axis() op_tan() op_tanh() op_tensordot() op_tile() op_top_k() op_trace() op_transpose() op_tri() op_tril() op_triu() op_unstack() op_var() op_vdot() op_vectorize() op_vectorized_map() op_vstack() op_where() op_while_loop() op_zeros() op_zeros_like()