Multi Input Data by name

Input Data in : array, is an array of clusters, where each cluster represents a single model input. Each cluster contains metadata and raw data required to describe and pass an input tensor to the model.

 input_name : string, specifies the identifier of the input. It corresponds to the name given to the input during its creation (via the optional name parameter).
Inputs Info : cluster

inputs_data : array, contains the raw byte representation of the input tensor data, stored as a 1D flattened buffer.
inputs_shapes : array, specifies the shape of the input tensor. Since the data is stored as a flattened 1D buffer, this shape is necessary to reconstruct the original dimensions.
inputs string length : array, used when the tensor type is string. If the tensor has shape [5,3], this field contains 15 values, each representing the length of a corresponding string element. This ensures that the actual size of inputs_data is known despite variable string lengths.
inputs_ranks : array, indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
inputs_types : array, defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).

Loss Input Data in : array, is an array of clusters, where each cluster represents a single model input. Each cluster contains metadata and raw data required to describe and pass an input tensor to the model.

 y_true_name : string, specifies the identifier of the input. It corresponds to the name given to the input during its creation (via the optional name parameter).
Inputs Info : cluster

inputs_data : array, contains the raw byte representation of the input tensor data, stored as a 1D flattened buffer.
inputs_shapes : array, specifies the shape of the input tensor. Since the data is stored as a flattened 1D buffer, this shape is necessary to reconstruct the original dimensions.
inputs string length : array, used when the tensor type is string. If the tensor has shape [5,3], this field contains 15 values, each representing the length of a corresponding string element. This ensures that the actual size of inputs_data is known despite variable string lengths.
inputs_ranks : array, indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
inputs_types : array, defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).

Fit Parameters : cluster

train_batch_size : integer, number of samples processed per batch during training.
num_train_epochs : integer, total number of passes over the entire dataset.
gradient_accumulation_steps : integer, number of steps to accumulate gradients before updating the weights.
learning_rate : float, learning rate used to update the model weights.