frontend/pytorch: add aten::cross_entropy_loss translator and tests (fixes #29691, relates #28584)

[Rahuldrabit]

1. Core Implementation

File: src/frontends/pytorch/src/op/cross_entropy_loss.cpp

Implements the translate_cross_entropy_loss function that converts PyTorch's cross_entropy_loss operation to OpenVINO operations. The implementation follows PyTorch's specification:

cross_entropy_loss = log_softmax + nll_loss

Key features:

• Applies LogSoftmax along dimension 1 (class dimension)
• Uses one-hot masking plus ReduceSum to select target class probabilities (NLL loss)
• Supports optional class weights via Gather + Multiply
• Supports three reduction modes:
  • none (0): No reduction, returns per-sample loss
  • mean (1): Mean of all losses (default)
  • sum (2): Sum of all losses
• Handles both 2D inputs (N, C) and higher-dimensional inputs (N, C, d1, d2, ...)

2. Operation Registration

File: src/frontends/pytorch/src/op_table.cpp

• Added forward declaration: OP_CONVERTER(translate_cross_entropy_loss);
• Registered operation: {"aten::cross_entropy_loss", op::translate_cross_entropy_loss},

3. Tests

File: tests/layer_tests/pytorch_tests/test_cross_entropy_loss.py

Comprehensive test suite covering:

• Basic functionality with different reduction modes (mean, sum, none)
• Class weights
• Different input shapes (2D and spatial dimensions)
• Edge cases (binary classification, single sample, many classes)