Shortening PyTorch Frontend Implementations

[xoiga123]

Most frontends should be implemented in very few lines of code, with many only being a single line of code. Shortening a frontend might involve adding a new function to our Experimental API, or extending the superset of an existing Ivy function. You can read more about how to do this here

Below is the list of frontend implementations with more than 10 lines of code (including lines of the helper functions used), sorted from high to low. This list will be updated once per week on Monday.

• [x] #23074
• [ ] cosine_embedding_loss - 46 LOC
• [ ] interpolate - 37 LOC
• [ ] triplet_margin_loss - 36 LOC
• [ ] affine_grid - 33 LOC
• [ ] kl_div - 27 LOC
• [ ] triplet_margin_with_distance_loss - 27 LOC
• [ ] isin - 26 LOC
• [ ] avg_pool2d - 24 LOC
• [ ] conv1d - 24 LOC
• [ ] conv2d - 24 LOC
• [ ] conv3d - 24 LOC
• [ ] fold - 24 LOC
• [ ] nll_loss - 24 LOC
• [ ] promote_types_of_torch_inputs - 23 LOC
• [ ] range - 22 LOC
• [ ] avg_pool1d - 22 LOC
• [ ] cross_entropy - 22 LOC
• [ ] multilabel_soft_margin_loss - 22 LOC
• [ ] smooth_l1_loss - 22 LOC
• [ ] max_pool1d - 22 LOC
• [ ] allclose - 21 LOC
• [ ] rot90 - 21 LOC
• [ ] huber_loss - 21 LOC
• [ ] poisson_nll_loss - 21 LOC
• [ ] isclose - 19 LOC
• [ ] #15099 - 19 LOC
• [ ] binary_cross_entropy - 19 LOC
• [ ] binary_cross_entropy_with_logits - 19 LOC
• [ ] hinge_embedding_loss - 19 LOC
• [ ] tensorinv - 18 LOC
• [ ] l1_loss - 18 LOC
• [ ] local_response_norm - 18 LOC
• [ ] margin_ranking_loss - 18 LOC
• [ ] mse_loss - 18 LOC
• [ ] soft_margin_loss - 18 LOC
• [ ] index_add - 17 LOC
• [ ] cosine_similarity - 16 LOC
• [ ] gaussian_nll_loss - 16 LOC
• [ ] renorm - 15 LOC
• [ ] unfold - 15 LOC
• [ ] pixel_unshuffle - 15 LOC
• [ ] index_copy - 14 LOC
• [ ] pixel_shuffle - 14 LOC
• [ ] unique - 14 LOC
• [ ] gather - 12 LOC
• [ ] vander - 12 LOC
• [ ] embedding - 12 LOC
• [ ] kthvalue - 11 LOC
• [ ] gumbel_softmax - 11 LOC
• [ ] promote_types_torch - 11 LOC
• [ ] addbmm - 10 LOC
• [ ] tile - 10 LOC
• [ ] inv_ex - 10 LOC
• [ ] solve_ex - 10 LOC
• [ ] solve_ex - 10 LOC
• [ ] solve_ex - 10 LOC
• [ ] vander - 12 LOC

[01PrathamS]

hey @AnnaTz i am beginner contributor looking forward to contribute to this repo, i gone through the docs and codebase i am familiar with PyTorch so i choose to start from here can you help me with shortening PyTorch frontend Implementation

[xoiga123]

@01PrathamS Please check the new docs section for this task. Thank you :hugs: