neulab/docprompting-conala huggingface dataset:
tf.raw_ops.MaxPool Performs max pooling on the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool  
tf.raw_ops.MaxPool(
    input, ksize, strides, padding, explicit_paddings=[],
    data_format='NHWC', name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32, int64, uint8, int16, int8, uint16, qint8. 4-D input to pool over.  
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  explicit_paddings   An optional list of ints. Defaults to [].  
  data_format   An optional string from: "NHWC", "NCHW", "NCHW_VECT_C". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.MaxPool3D Performs 3D max pooling on the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool3D  
tf.raw_ops.MaxPool3D(
    input, ksize, strides, padding, data_format='NDHWC', name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32. Shape [batch, depth, rows, cols, channels] tensor to pool over.  
  ksize   A list of ints that has length >= 5. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.MaxPool3DGrad Computes gradients of 3D max pooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool3DGrad  
tf.raw_ops.MaxPool3DGrad(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NDHWC', name=None
)

 


 Args
  orig_input   A Tensor. Must be one of the following types: half, bfloat16, float32. The original input tensor.  
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.  
  grad   A Tensor. Must be one of the following types: half, bfloat16, float32. Output backprop of shape [batch, depth, rows, cols, channels].  
  ksize   A list of ints that has length >= 5. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as grad.
tf.raw_ops.MaxPool3DGradGrad Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool3DGradGrad  
tf.raw_ops.MaxPool3DGradGrad(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NDHWC', name=None
)

 


 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.  
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.  
  grad   A Tensor. Must have the same type as orig_input. Output backprop of shape [batch, depth, rows, cols, channels].  
  ksize   A list of ints that has length >= 5. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as orig_input.
tf.raw_ops.MaxPoolGrad Computes gradients of the maxpooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGrad  
tf.raw_ops.MaxPoolGrad(
    orig_input, orig_output, grad, ksize, strides, padding, explicit_paddings=[],
    data_format='NHWC', name=None
)

 


 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.  
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.  
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients w.r.t. the output of max_pool.  
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  explicit_paddings   An optional list of ints. Defaults to [].  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as orig_input.
tf.raw_ops.MaxPoolGradGrad Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradGrad  
tf.raw_ops.MaxPoolGradGrad(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NHWC', name=None
)

 


 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.  
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.  
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients of gradients w.r.t. the input of max_pool.  
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as orig_input.
tf.raw_ops.MaxPoolGradGradV2 Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradGradV2  
tf.raw_ops.MaxPoolGradGradV2(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NHWC', name=None
)

 


 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.  
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.  
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients of gradients w.r.t. the input of max_pool.  
  ksize   A Tensor of type int32. The size of the window for each dimension of the input tensor.  
  strides   A Tensor of type int32. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as orig_input.
tf.raw_ops.MaxPoolGradGradWithArgmax Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradGradWithArgmax  
tf.raw_ops.MaxPoolGradGradWithArgmax(
    input, grad, argmax, ksize, strides, padding, include_batch_in_index=False,
    name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input.  
  grad   A Tensor. Must have the same type as input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the input of max_pool.  
  argmax   A Tensor. Must be one of the following types: int32, int64. The indices of the maximum values chosen for each output of max_pool.  
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  include_batch_in_index   An optional bool. Defaults to False. Whether to include batch dimension in flattened index of argmax.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.MaxPoolGradV2 Computes gradients of the maxpooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradV2  
tf.raw_ops.MaxPoolGradV2(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NHWC', name=None
)

 


 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.  
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.  
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients w.r.t. the output of max_pool.  
  ksize   A Tensor of type int32. The size of the window for each dimension of the input tensor.  
  strides   A Tensor of type int32. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as orig_input.
tf.raw_ops.MaxPoolGradWithArgmax Computes gradients of the maxpooling function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradWithArgmax  
tf.raw_ops.MaxPoolGradWithArgmax(
    input, grad, argmax, ksize, strides, padding, include_batch_in_index=False,
    name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input.  
  grad   A Tensor. Must have the same type as input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of max_pool.  
  argmax   A Tensor. Must be one of the following types: int32, int64. The indices of the maximum values chosen for each output of max_pool.  
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  include_batch_in_index   An optional bool. Defaults to False. Whether to include batch dimension in flattened index of argmax.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.MaxPoolV2 Performs max pooling on the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolV2  
tf.raw_ops.MaxPoolV2(
    input, ksize, strides, padding, data_format='NHWC', name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32, int64, uint8, int16, int8, uint16, qint8. 4-D input to pool over.  
  ksize   A Tensor of type int32. The size of the window for each dimension of the input tensor.  
  strides   A Tensor of type int32. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NHWC", "NCHW", "NCHW_VECT_C". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.MaxPoolWithArgmax Performs max pooling on the input and outputs both max values and indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolWithArgmax  
tf.raw_ops.MaxPoolWithArgmax(
    input, ksize, strides, padding, Targmax=tf.dtypes.int64,
    include_batch_in_index=False, name=None
)
 The indices in argmax are flattened, so that a maximum value at position [b, y, x, c] becomes flattened index: (y * width + x) * channels + c if include_batch_in_index is False; ((b * height + y) * width + x) * channels + c if include_batch_in_index is True. The indices returned are always in [0, height) x [0, width) before flattening, even if padding is involved and the mathematically correct answer is outside (either negative or too large). This is a bug, but fixing it is difficult to do in a safe backwards compatible way, especially due to flattening.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 4-D with shape [batch, height, width, channels]. Input to pool over.  
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.  
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  Targmax   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.  
  include_batch_in_index   An optional bool. Defaults to False. Whether to include batch dimension in flattened index of argmax.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, argmax).     output   A Tensor. Has the same type as input.  
  argmax   A Tensor of type Targmax.
tf.raw_ops.Mean Computes the mean of elements across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Mean  
tf.raw_ops.Mean(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The tensor to reduce.  
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).  
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Merge Forwards the value of an available tensor from inputs to output.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Merge  
tf.raw_ops.Merge(
    inputs, name=None
)
 Merge waits for at least one of the tensors in inputs to become available. It is usually combined with Switch to implement branching. Merge forwards the first tensor to become available to output, and sets value_index to its index in inputs.
 


 Args
  inputs   A list of at least 1 Tensor objects with the same type. The input tensors, exactly one of which will become available.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, value_index).     output   A Tensor. Has the same type as inputs.  
  value_index   A Tensor of type int32.
tf.raw_ops.MergeSummary Merges summaries.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MergeSummary  
tf.raw_ops.MergeSummary(
    inputs, name=None
)
 This op creates a Summary protocol buffer that contains the union of all the values in the input summaries. When the Op is run, it reports an InvalidArgument error if multiple values in the summaries to merge use the same tag.
 


 Args
  inputs   A list of at least 1 Tensor objects with type string. Can be of any shape. Each must contain serialized Summary protocol buffers.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.MergeV2Checkpoints V2 format specific: merges the metadata files of sharded checkpoints. The  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.MergeV2Checkpoints  
tf.raw_ops.MergeV2Checkpoints(
    checkpoint_prefixes, destination_prefix, delete_old_dirs=True, name=None
)
 result is one logical checkpoint, with one physical metadata file and renamed data files. Intended for "grouping" multiple checkpoints in a sharded checkpoint setup. If delete_old_dirs is true, attempts to delete recursively the dirname of each path in the input checkpoint_prefixes. This is useful when those paths are non user-facing temporary locations.
 


 Args
  checkpoint_prefixes   A Tensor of type string. prefixes of V2 checkpoints to merge.  
  destination_prefix   A Tensor of type string. scalar. The desired final prefix. Allowed to be the same as one of the checkpoint_prefixes.  
  delete_old_dirs   An optional bool. Defaults to True. see above.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.Mfcc Transforms a spectrogram into a form that's useful for speech recognition.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Mfcc  
tf.raw_ops.Mfcc(
    spectrogram, sample_rate, upper_frequency_limit=4000, lower_frequency_limit=20,
    filterbank_channel_count=40, dct_coefficient_count=13, name=None
)
 Mel Frequency Cepstral Coefficients are a way of representing audio data that's been effective as an input feature for machine learning. They are created by taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the higher frequencies that are less significant to the human ear. They have a long history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum is a good resource to learn more.
 


 Args
  spectrogram   A Tensor of type float32. Typically produced by the Spectrogram op, with magnitude_squared set to true.  
  sample_rate   A Tensor of type int32. How many samples per second the source audio used.  
  upper_frequency_limit   An optional float. Defaults to 4000. The highest frequency to use when calculating the ceptstrum.  
  lower_frequency_limit   An optional float. Defaults to 20. The lowest frequency to use when calculating the ceptstrum.  
  filterbank_channel_count   An optional int. Defaults to 40. Resolution of the Mel bank used internally.  
  dct_coefficient_count   An optional int. Defaults to 13. How many output channels to produce per time slice.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.Min Computes the minimum of elements across dimensions of a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Min  
tf.raw_ops.Min(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64, qint8, quint8, qint32, qint16, quint16. The tensor to reduce.  
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).  
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.